Search results for: mixed effect logistic regression model

Authors: Peter Temitope Agboola

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Food insecurity continues to affect a large number of U.S households during this coronavirus COVID-19 pandemic. The pandemic has threatened the livelihoods of people, making them vulnerable to severe hardship and has had an unanticipated impact on the U.S economy. This study attempts to identify the food insecurity status of households and the determinant factors driving household food insecurity. Additionally, it attempts to discover the mitigation measures adopted by households during the pandemic in the city of Huntsville, Texas. A structured online sample survey was used to collect data, with a household expenditures survey used in evaluating the food security status of the household. Most survey respondents disclosed that the COVID-19 pandemic had affected their life and source of income. Furthermore, the main analytical tool used for the study is descriptive statistics and logistic regression modeling. A logistic regression model was used to determine the factors responsible for food insecurity in the study area. The result revealed that most households in the study area are food secure, with the remainder being food insecure.

Keywords: food insecurity, household expenditure survey, COVID-19, coping strategies, food pantry

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Authors: Tsedeke Lambore Gemecho, Ayele Taye Goshu

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The main objective of this study is to identify the determinants of the number of international migrants in a household and to compare regression models for count response. This study is done by collecting data from total of 2288 household heads of 16 randomly sampled districts in Hadiya and Kembata-Tembaro zones of Southern Ethiopia. The Poisson mixed models, as special cases of the generalized linear mixed model, is explored to determine effects of the predictors: age of household head, farm land size, and household size. Two ethnicities Hadiya and Kembata are included in the final model as dummy variables. Stepwise variable selection has indentified four predictors: age of head, farm land size, family size and dummy variable ethnic2 (0=other, 1=Kembata). These predictors are significant at 5% significance level with count response number of migrant. The Poisson mixed model consisting of the four predictors with random effects districts. Area specific random effects are significant with the variance of about 0.5105 and standard deviation of 0.7145. The results show that the number of migrant increases with heads age, family size, and farm land size. In conclusion, there is a significantly high number of international migration per household in the area. Age of household head, family size, and farm land size are determinants that increase the number of international migrant in households. Community-based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.

Keywords: Poisson regression, GLM, number of migrant, Hadiya and Kembata Tembaro zones

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Authors: Yi-Cheng Tian, Miin-Shen Yang

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The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

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Authors: D. S. Gomes, A. T. Silva

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Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

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Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

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Authors: Daming Xu, Yuanyuan Wang

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Abstract: Under the trend of rapid expansion of urbanization, the motorized urban characteristics become more and more obvious, and the walkability of urban space is seriously affected. The construction of walkability of space, as the main mode of travel for the elderly in their daily lives, has become more and more important in the current social context of serious aging. Settlement is the most basic living unit of residents, and daily shopping, medical care, and other daily trips are closely related to the daily life of the elderly. Therefore, it is of great practical significance to explore the impact of built environment on elderly people's daily walking trips at the settlement level for the construction of pedestrian-friendly settlements for the elderly. The study takes three typical settlements in Harbin Daoli District in three different periods as examples and obtains data on elderly people's walking trips and built environment characteristics through field research, questionnaire distribution, and internet data acquisition. Finally, correlation analysis and multinomial logistic regression model were applied to analyze the influence mechanism of built environment on elderly people's walkability based on the control of personal attribute variables in order to provide reference and guidance for the construction of walkability for elderly people in built environment in the future.

Keywords: built environment, elderly, walkability, multinomial logistic regression model

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the spark plug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria in terms of its GDP.

Keywords: maritime transport, economy, GDP, regression, port

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Authors: Marybec Griffin

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Purpose: The purpose of this cross-sectional study was to get a better understanding of healthcare usage and satisfaction among young adult gay men (YAGM), including the facility used as the usual source of healthcare, preference for coordinated healthcare, and if their primary care provider (PCP) adequately addressed the health needs of gay men. Methods: Interviews were conducted among n=800 YAGM in New York City (NYC). Participants were surveyed about their sociodemographic characteristics and healthcare usage and satisfaction access using multivariable logistic regression models. The surveys were conducted between November 2015 and June 2016. Results: The mean age of the sample was 24.22 years old (SD=4.26). The racial and ethnic background of the participants is as follows: 35.8% (n=286) Black Non-Hispanic, 31.9% (n=225) Hispanic/Latino, 20.5% (n=164) White Non-Hispanic, 4.4% (n=35) Asian/Pacific Islander, and 6.9% (n=55) reporting some other racial or ethnic background. 31.1% (n=249) of the sample had an income below $14,999. 86.7% (n=694) report having either public or private health insurance. For usual source of healthcare, 44.6% (n=357) of the sample reported a private doctor’s office, 16.3% (n=130) reported a community health center, and 7.4% (n=59) reported an urgent care facility, and 7.6% (n=61) reported not having a usual source of healthcare. 56.4% (n=451) of the sample indicated a preference for coordinated healthcare. 54% (n=334) of the sample were very satisfied with their healthcare. Findings from multivariable logistical regression models indicate that participants with higher incomes (AOR=0.54, 95% CI 0.36-0.81, p < 0.01) and participants with a PCP (AOR=0.12, 95% CI 0.07-0.20, p < 0.001) were less likely to use a walk-in facility as their usual source of healthcare. Results from the second multivariable logistic regression model indicated that participants who experienced discrimination in a healthcare setting were less likely to prefer coordinated healthcare (AOR=0.63, 95% CI 0.42-0.96, p < 0.05). In the final multivariable logistic model, results indicated that participants who had disclosed their sexual orientation to their PCP (AOR=2.57, 95% CI 1.25-5.21, p < 0.01) and were comfortable discussing their sexual activity with their PCP (AOR=8.04, 95% CI 4.76-13.58, p < 0.001) were more likely to agree that their PCP adequately addressed the healthcare needs of gay men. Conclusion: Understanding healthcare usage and satisfaction among YAGM is necessary as the healthcare landscape changes, especially given the relatively recent addition of urgent care facilities. The type of healthcare facility used as a usual source of care influences the ability to seek comprehensive and coordinated healthcare services. While coordinated primary and sexual healthcare may be ideal, individual preference for this coordination among YAGM is desired but may be limited due to experiences of discrimination in primary care settings.

Keywords: healthcare policy, gay men, healthcare access, Affordable Care Act

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Yibeltal T. Bayou, Yohana S. Mashalla, Gloria Thupayagale-Tshweneagae

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Background: According to the World Health Organization, caesarean section delivery rates of more than 10-15% caesarean section deliveries in any specific geographic region in the world are not justifiable. The aim of the study was to describe the level and analyse determinants of caesarean section delivery in Addis Ababa. Methods: Data was collected in Addis Ababa using a structured questionnaire administered to 901 women aged 15-49 years through a stratified two-stage cluster sampling technique. Binary logistic regression model was employed to identify predictors of caesarean section delivery. Results: Among the 835 women who delivered their last birth at healthcare facilities, 19.2% of them gave birth by caesarean section. About 9.0% of the caesarean section births were due to mother’s request or service provider’s influence without any medical indication. The caesarean section delivery rate was much higher than the recommended rate particularly among the non-slum residents (27.2%); clients of private healthcare facilities (41.1%); currently married women (20.6%); women with secondary (22.2%) and tertiary (33.6%) level of education; and women belonging to the highest wealth quintile household (28.2%). The majority (65.8%) of the caesarean section clients were not informed about the consequences of caesarean section delivery by service providers. The logistic regression model shows that older age (30-49), secondary and above education, non-slum residence, high-risk pregnancy and receiving adequate antenatal care were significantly positively associated with caesarean section delivery. Conclusion: Despite the unreserved effort towards achieving MDG 5 through safe skilled delivery assistance among others, the high caesarean section rate beyond the recommend limit, and the finding that caesarean sections done without medical indications were also alarming. The government and city administration should take appropriate measures before the problems become setbacks in healthcare provision. Further investigations should focus on the effect of caesarean section delivery on maternal and child health outcomes in the study area.

Keywords: Addis Ababa, caesarean section, mode of delivery, slum residence

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Benchamat Laksaniyanon, Padcharee Phasuk, Rungtawan Boonphanakan

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With globalization, organizations had to adjust to an unstable environment in order to survive in a competitive arena. Typically within the field of management, different types of organizations include mechanistic, bureaucratic and organic ones. In fact, bureaucratic and mechanistic organizations have some characteristics in common. Bureaucracy is one type of Thailand organization which adapted from mechanistic concept to develop an organization that is suitable for the characteristic and culture of Thailand. The objective of this study is to compare the adjustment strategies of both organizations in order to find key performance indicators (KPI) suitable for improving organization in Thailand. The methodology employed is binary logistic regression. The results of this study will be valuable for developing future management strategies for both bureaucratic and mechanistic organizations.

Keywords: mechanistic, bureaucratic and organic organization, binary logistic regression, key performance indicators (KPI)

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Authors: Masood Beheshtirad

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Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed.

Keywords: landslide, mapping, multiple model, regression

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Authors: Hassnen M. Jafer, Khalid S. Hashim, W. Atherton, Ali W. Alattabi

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This study investigates the effect of two important parameters (length of curing period and percentage of the added binder) on the strength of soil treated with OPC. An intermediate plasticity silty clayey soil with medium organic content was used in this study. This soft soil was treated with different percentages of a commercially available cement type 32.5-N. laboratory experiments were carried out on the soil treated with 0, 1.5, 3, 6, 9, and 12% OPC by the dry weight to determine the effect of OPC on the compaction parameters, consistency limits, and the compressive strength. Unconfined compressive strength (UCS) test was carried out on cement-treated specimens after exposing them to different curing periods (1, 3, 7, 14, 28, and 90 days). The results of UCS test were used to develop a non-linear multi-regression model to find the relationship between the predicted and the measured maximum compressive strength of the treated soil (qu). The results indicated that there was a significant improvement in the index of plasticity (IP) by treating with OPC; IP was decreased from 20.2 to 14.1 by using 12% of OPC; this percentage was enough to increase the UCS of the treated soil up to 1362 kPa after 90 days of curing. With respect to the statistical model of the predicted qu, the results showed that the regression coefficients (R2) was equal to 0.8534 which indicates a good reproducibility for the constructed model.

Keywords: cement admixtures, soft soil stabilisation, geotechnical parameters, multi-regression model

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: Jagadish Rao Padubidri, Mehak Bhandary, Sowmya J. Rao

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Introduction: The significance of the human foot and its measurements in identifying an individual has been proved a lot of times by different studies in different geographical areas and its association to the stature and gender of the individual has been justified by many researches. In our study we have used different foot measurements including the length, width, malleol height and navicular height for establishing its association to stature and gender and to find out its accuracy. The purpose of this study is to show the relation of foot measurements with stature and gender, and to derive Multiple and Logistic regression equations for stature and gender estimation in South Indian population. Materials and Methods: The subjects for this study were 200 South Indian students out of which 100 were females and 100 were males, aged between 18 to 24 years. The data for the present study included the stature, foot length, foot breath, foot malleol height, foot navicular height of both right and left foot. Descriptive statistics, T-test and Pearson correlation coefficients were derived between stature, gender and foot measurements. The stature was estimated from right and left foot measurements for both male and female South Indian population using multiple regression analysis and logistic regression analysis for gender estimation. Results: The means, standard deviation, stature, right and left foot measurements and T-test in male population were higher than in females. LFL (Left foot length) is more than RFL (Right Foot length) in male groups, but in female groups the length of both foot are almost equal [RFL=226.6, LFL=227.1]. There is not much of difference in means of RFW (Right foot width) and LFW (Left foot width) in both the genders. Significant difference were seen in mean values of malleol and navicular height of right and left feet in male gender. No such difference was seen in female subjects. Conclusions: The study has successfully demonstrated the correlation of foot length in stature estimation in all the three study groups in both right and left foot. Next in parameters are Foot width and malleol height in estimating stature among male and female groups. Navicular height of both right and left foot showed poor relationship with stature estimation in both male and female groups. Multiple regression equations for both right and left foot measurements to estimate stature were derived with standard error ranging from 11-12 cm in males and 10-11 cm in females. The SEE was 5.8 when both male and female groups were pooled together. The logistic regression model which was derived to determine gender showed 85% accuracy and 92.5% accuracy using right and left foot measurements respectively. We believe that stature and gender can be estimated with foot measurements in South Indian population.

Keywords: foot length, gender, stature, South Indian

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Authors: Mukund Ramchandra Mogarekar, Pankaj Kumar, Shraddha Vilas More

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Background: Myocardial infarction (MI) is defined as myocardial cell death due to prolonged ischemia as a consequence of atherosclerosis. TC, low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), Apo B, and lipoprotein(a) was found as atherogenic factors while high-density lipoprotein cholesterol (HDL-C) was anti-atherogenic. Methods and Results: The study group consists of 40, MI subjects and 40 healthy individuals in control group. PON 1 Arylesterase activity (ARE) was measured by using phenylacetate. Phenotyping was done by double substrate method, serum AOPP by using chloramine T and Apo B by Turbidimetric immunoassay. PON 1 ARE activities were significantly lower (p< 0.05) and AOPPs & Apo B were higher in MI subjects (p> 0.05). Trimodal distribution of QQ, QR, and RR phenotypes of study population showed no significant difference among cases and controls (p> 0.05). Univariate binary logistic regression analysis showed independent association of TC, HDL, LDL, AOPP, Apo B, and PON 1 ARE activity with MI and multiple forward binary logistic regression showed PON 1 ARE activity and serum Apo B as an independent predictor of MI. Conclusions: Decrease in PON 1 ARE activity in MI subjects than in controls suggests increased oxidative stress in MI which is reflected by significantly increased AOPP and Apo B. PON1 polymorphism of QQ, QR and RR showed no significant difference in protection against MI. Univariate and multiple binary logistic regression showed PON1 ARE activity and serum Apo B as an independent predictor of MI.

Keywords: advanced oxidation protein product, apolipoprotein B, PON 1 arylesterase activity, myocardial infarction

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Authors: Guo Yu, Haoming Ma, Peiru Zhou

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BACKGROUND: In addition to hyperglycemia, hypoglycemia, and glycemic variability, a decrease in the time in the targeted blood glucose range (TIR) may be associated with an increased risk of death for critically ill patients. However, the relationship between the TIR and mortality may be influenced by the presence of diabetes and glycemic variability. METHODS: A total of 998 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The TIR is defined as the percentage of time spent in the target blood glucose range of 3.9–10.0 mmol/L within 24 hours. The relationship between TIR and in-hospital in diabetic and non-diabetic patients was analyzed. The effect of glycemic variability was also analyzed. RESULTS: The binary logistic regression model showed that there was a significant association between the TIR as a continuous variable and the in-hospital death of severely ill non-diabetic patients (OR=0.991, P=0.015). As a classification variable, TIR≥70% was significantly associated with in-hospital death (OR=0.581, P=0.003). Specifically, TIR≥70% was a protective factor for the in-hospital death of severely ill non-diabetic patients. The TIR of severely ill diabetic patients was not significantly associated with in-hospital death; however, glycemic variability was significantly and independently associated with in-hospital death (OR=1.042, P=0.027). Binary logistic regression analysis of comprehensive indices showed that for non-diabetic patients, the C3 index (low TIR & high CV) was a risk factor for increased mortality (OR=1.642, P<0.001). In addition, for diabetic patients, the C3 index was an independent risk factor for death (OR=1.994, P=0.008), and the C4 index (low TIR & low CV) was independently associated with increased survival. CONCLUSIONS: The TIR of non-diabetic patients during ICU hospitalization was associated with in-hospital death even after adjusting for disease severity and glycemic variability. There was no significant association between the TIR and mortality of diabetic patients. However, for both diabetic and non-diabetic critically ill patients, the combined effect of high TIR and low CV was significantly associated with ICU mortality. Diabetic patients seem to have higher blood glucose fluctuations and can tolerate a large TIR range. Both diabetic and non-diabetic critically ill patients should maintain blood glucose levels within the target range to reduce mortality.

Keywords: severe disease, diabetes, blood glucose control, time in targeted blood glucose range, glycemic variability, mortality

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Authors: Hurgesa Hundera, Samuel Shibeshibikeko, Tarike Daba, Tesfaye Ganamo

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The study aimed at examining the ongoing adaptation strategies used by smallholder farmers in response to climate variability in Boset district. It also assessed the socioeconomic factors that influence the choice of adaptation strategies of smallholder farmers to climate variability risk. For attaining the objectives of the study, both primary and secondary sources of data were employed. The primary data were obtained through a household questionnaire, key informant interviews, focus group discussions, and observations, while secondary data were acquired through desk review. Questionnaires were distributed and filled by 328 respondents, and they were identified through systematic random sampling technique. Descriptive statistics and binary logistic regression model were applied in this study as the main analytical methods. The findings of the study reveal that the sample households have utilized multiple adaptation strategies in response to climate variability, such as cropping early mature crops, planting drought resistant crops, growing mixed crops on the same farm lands, and others. The results of the binary logistic model revealed that education, sex, age, family size, off farm income, farm experience, access to climate information, access to farm input, and farm size were significant and key factors determining farmers’ choice of adaptation strategies to climate variability in the study area. To enable effective adaptation measures, Ministry of Agriculture and Natural Resource, with its regional bureaus and offices and concerned non–governmental organizations, should consider climate variability in their planning and budgeting in all levels of decision making.

Keywords: adaptation strategies, boset district, climate variability, smallholder farmers

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Authors: Mukund Ramchandra Mogarekar, Pankaj Kumar, Shraddha V. More

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Background: Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), Apo B, and lipoprotein(a) was found as atherogenic factors while high-density lipoprotein cholesterol (HDL-C) was anti-atherogenic. Methods and Results: The study group consists of 40 MI subjects as cases and 40 healthy as controls. Monocytic PON 2 Lactonase (LACT) activity was measured by using Dihydrocoumarine (DHC) as substrate. Phenotyping was done by method of Mogarekar MR et al, serum AOPP by modified method of Witko-Sarsat V et al and Apo B by Turbidimetric immunoassay. PON 2 LACT activities were significantly lower (p< 0.05) and AOPPs & Apo B were higher in MI subjects (p> 0.05). Trimodal distribution of QQ, QR & RR phenotypes of study population showed no significant difference among cases and controls (p> 0.05). Univariate binary logistic regression analysis showed independent association of TC, HDL, LDL, AOPP, Apo B, and PON 2 LACT activity with MI and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI. Conclusions- Decrease in PON 2 LACT activity in MI subjects than in controls suggests increased oxidative stress in MI which is reflected by significantly increased AOPP and Apo B. PON 1 polymorphism of QQ, QR and RR showed no significant difference in protection against MI. Univariate and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI.

Keywords: advanced oxidation protein products, apolipoprotein-B, myocardial infarction, paraoxonase 2 lactonase

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Lovro Štefan

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The main purpose of the present study was to investigate the associations between the overweight/obesity status and lifestyle factors. In this cross-sectional study, participants were 1950 urban secondary-school students (54.7% of female students) aged 17-18 years old. Dependent variable was body-mass index status derived from self-reported height and weight. The outcome was binarised, where participants with value <25 kg/m2 were collapsed into „normal“, while those ≥25 kg/m2 into „overweight/obesity“ category. Independent variables were gender, type of school, physical activity, sedentary behaviour, self-rated health, self-perceived socioeconomic status and psychological distress. The associations between the dependent and independent variables were analyzed by using multiple logistic regression analysis. In the univariate model, being overweight/obese was significantly associated with being a male student (OR 0.31; 95% CI 0.23 to 0.42), attending a vocational school (OR 1.87; 95% CI 1.42 to 2.48), not meeting the recommendations for moderate-to-vigorous physical activity (OR 0.44; 95% CI 0.22 to 0.88), more time spending in sedentary behaviour (OR 1.53; 95% CI 1.07 to 2.19), poor self-rated health (OR 0.35, 95% CI 0.20 to 0.56) and lower socioeconomic status (OR 0.63; 95% CI 0.48 to 0.84). In the multivariate model, the same associations occured between the dependent and independent variable. In both models, psychological distress was not associated with being overweight/obese. In conclusion, our findings suggest, that lifestyle factors are independently associated with body-mass index

Keywords: body mass index, secondary-school students, Croatia, physical activity, sedentary behaviour, logistic regression

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Authors: Lazaro E. Nnko, Japhet J. Kashaigili, Gerald C. Monela, Pantaleo K. T. Munishi

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Agroforestry systems and practices are perceived to improve livelihood and sustainable management of natural resources. However, their adoption in various regions differs with the biophysical conditions and societal characteristics. This study was conducted in Kilombero District to investigate the factors influencing the adoption of different agroforestry systems and practices in agro-ecosystems and farming systems. A household survey, key informant interviews, and focus group discussion was used for data collection in three villages. Descriptive statistics and multinomial logistic regression in SPSS were applied for analysis. Results show that Igima and Ngajengwa villages had home garden practices dominated, as revealed by 63.3% and 66.7%, respectively, while Mbingu village had mixed intercropping practice with 56.67%. Agrosilvopasture systems were dominant in Igima and Ngajengwa villages with 56.7% and 66.7%, respectively, while in Mbingu village, the dominant system was agrosilviculture with 66.7%. The results from multinomial logistic regression show that different explanatory variable was statistical significance as predictors of the adoption of agroforestry systems and practices. Residence type and sex were the most dominant factor influencing the adoption of agroforestry systems. Duration of stay in the village, availability of extension education, residence, and sex were the dominant factor influencing the adoption of agroforestry practices. The most important and statistically significant factors among these were residence type and sex. The study concludes that agroforestry will be more successful if the local priorities, which include social-economic need characteristics of the society, will be considered in designing systems and practices. The socio-economic need of the community should be addressed in the process of expanding the adoption of agroforestry systems and practices.

Keywords: agroforestry adoption, agroforestry systems, agroforestry practices, agroforestry, Kilombero

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Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

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This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

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Authors: Haoming Ma, Guo Yu, Peiru Zhou

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Background: Previous studies show that dysglycemia, mostly hyperglycemia, hypoglycemia and glycemic variability(GV), are associated with excess mortality in critically ill patients, especially those without diabetes. Glycemic variability is an increasingly important measure of glucose control in the intensive care unit (ICU) due to this association. However, there is limited data pertaining to the relationship between different clinical factors and glycemic variability and clinical outcomes categorized by their DM status. This retrospective study of 958 intensive care unit(ICU) patients was conducted to investigate the relationship between GV and outcome in critically ill patients and further to determine the significant factors that contribute to the glycemic variability. Aim: We hypothesize that the factors contributing to mortality and the glycemic variability are different from critically ill patients with or without diabetes. And the primary aim of this study was to determine which dysglycemia (hyperglycemia\hypoglycemia\glycemic variability) is independently associated with an increase in mortality among critically ill patients in different groups (DM/Non-DM). Secondary objectives were to further investigate any factors affecting the glycemic variability in two groups. Method: A total of 958 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The glycemic variability was defined as the coefficient of variation (CV) of blood glucose. The main outcome was death during hospitalization. The secondary outcome was GV. The logistic regression model was used to identify factors associated with mortality. The relationships between GV and other variables were investigated using linear regression analysis. Results: Information on age, APACHE II score, GV, gender, in-ICU treatment and nutrition was available for 958 subjects. Predictors remaining in the final logistic regression model for mortality were significantly different in DM/Non-DM groups. Glycemic variability was associated with an increase in mortality in both DM(odds ratio 1.05; 95%CI:1.03-1.08,p＜0.001) or Non-DM group(odds ratio 1.07; 95%CI:1.03-1.11,p=0.002). For critically ill patients without diabetes, factors associated with glycemic variability included APACHE II score(regression coefficient, 95%CI:0.29,0.22-0.36,p＜0.001), Mean BG(0.73,0.46-1.01,p＜0.001), total parenteral nutrition(2.87,1.57-4.17,p＜0.001), serum albumin(-0.18,-0.271 to -0.082,p＜0.001), insulin treatment(2.18,0.81-3.55,p=0.002) and duration of ventilation(0.006,0.002-1.010,p=0.003).However, for diabetes patients, APACHE II score(0.203,0.096-0.310,p＜0.001), mean BG(0.503,0.138-0.869,p=0.007) and duration of diabetes(0.167,0.033-0.301,p=0.015) remained as independent risk factors of GV. Conclusion: We found that the relation between dysglycemia and mortality is different in the diabetes and non-diabetes groups. And we confirm that GV was associated with excess mortality in DM or Non-DM patients. Furthermore, APACHE II score, Mean BG, total parenteral nutrition, serum albumin, insulin treatment and duration of ventilation were significantly associated with an increase in GV in Non-DM patients. While APACHE II score, mean BG and duration of diabetes (years) remained as independent risk factors of increased GV in DM patients. These findings provide important context for further prospective trials investigating the effect of different clinical factors in critically ill patients with or without diabetes.

Keywords: diabetes, glycemic variability, predictors, severe disease

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Authors: Nurten Akgun, Dilum Dissanayake, Neil Thorpe, Margaret C. Bell

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Cycling has gained a great attention with comparable speeds, low cost, health benefits and reducing the impact on the environment. The main challenge associated with cycling is the provision of safety for the people choosing to cycle as their main means of transport. From the road safety point of view, cyclists are considered as vulnerable road users because they are at higher risk of serious casualty in the urban network but more specifically at roundabouts. This research addresses the development of an enhanced mathematical model by including a broad spectrum of casualty related variables. These variables were geometric design measures (approach number of lanes and entry path radius), speed limit, meteorological condition variables (light, weather, road surface) and socio-demographic characteristics (age and gender), as well as contributory factors. Contributory factors included driver’s behavior related variables such as failed to look properly, sudden braking, a vehicle passing too close to a cyclist, junction overshot, failed to judge other person’s path, restart moving off at the junction, poor turn or manoeuvre and disobeyed give-way. Tyne and Wear in the UK were selected as a case study area. The cyclist casualty data was obtained from UK STATS19 National dataset. The reference categories for the regression model were set to slight and serious cyclist casualties. Therefore, binary logistic regression was applied. Binary logistic regression analysis showed that approach number of lanes was statistically significant at the 95% level of confidence. A higher number of approach lanes increased the probability of severity of cyclist casualty occurrence. In addition, sudden braking statistically significantly increased the cyclist casualty severity at the 95% level of confidence. The result concluded that cyclist casualty severity was highly related to approach a number of lanes and sudden braking. Further research should be carried out an in-depth analysis to explore connectivity of sudden braking and approach number of lanes in order to investigate the driver’s behavior at approach locations. The output of this research will inform investment in measure to improve the safety of cyclists at roundabouts.

Keywords: binary logistic regression, casualty severity, cyclist safety, roundabout

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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