Search results for: multivariate logistic regression

Authors: Cecília M. Antão, Paulo Jorge Nogueira

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Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate.

Keywords: breast cancer, comorbidities, logistic regression, adjusted odds ratio

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

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Authors: Li-Ching Chen

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The case-control design has been widely applied in clinical and epidemiological studies to investigate the association between risk factors and a given disease. The retrospective design can be easily implemented and is more economical over prospective studies. To adjust effects for confounding factors, methods such as stratification at the design stage and may be adopted. When some major confounding factors are difficult to be quantified, a matching design provides an opportunity for researchers to control the confounding effects. The matching effects can be parameterized by the intercepts of logistic models and the conditional logistic regression analysis is then adopted. This study demonstrates an information-matrix-based goodness-of-fit statistic to test the validity of the logistic regression model for matched case-control data. The asymptotic null distribution of this proposed test statistic is inferred. It needs neither to employ a simulation to evaluate its critical values nor to partition covariate space. The asymptotic power of this test statistic is also derived. The performance of the proposed method is assessed through simulation studies. An example of the real data set is applied to illustrate the implementation of the proposed method as well.

Keywords: conditional logistic model, goodness-of-fit, information matrix, matched case-control studies

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Authors: Swati Srivastava, Ashish Kumar Upadhyay

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Background: According to United Nation Children’s Fund, it has been estimated that worldwide about 165 million children were stunted in 2012 and India alone accounts for 38% of global burden of stunting. In terms of incidence, India is home of more than 60 million stunted children worldwide. Our study aims to examine the effect of pregnancy intention and maternal postnatal depressive symptoms on early childhood stunting in India. We hypothesized that effect of pregnancy intention and postnatal maternal depressive symptoms were mediated by social support. Methods: We used data from first wave of Young Lives Study India. Out of 2011 children recruited in original cohort, 1833 children had complete information on pregnancy intention, maternal depression and other variables. A series of multivariate logistic regression model were used to examine the effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting. Results: Bivariate result indicates that a higher percent of children born after unintended pregnancy (40%) were stunted than children of intended pregnancy (26%). Likewise, proportion of stunted children was also higher among women of high postnatal depressive symptoms (35%) than low level of depression (24%). Results of multivariate logistic regression model indicate that children born after unintended pregnancy were significantly more likely to be stunted than children born after intended pregnancy (Coefficient: 1.70, CI: 1.17, 2.48). Likewise, early childhood stunting was also associated with maternal postnatal depressive symptoms among women (Coefficient: 1.48, CI: 1.16, 1.88). The effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting remains unchanged after controlling for social support and other variables. Conclusions: The findings of this study provide conclusive evidence regarding consequences of pregnancy intention and postnatal depressive symptoms on early childhood stunting in India. Therefore, there is need to identify the women with unintended pregnancy and incorporate the promotion of mental health into their national reproductive and child health programme.

Keywords: pregnancy intention, postnatal depressive symptoms, social support, childhood stunting, young lives study, India

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Authors: Lina Wu, Wenyi Lu, Ye Li

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Regarding heavy video game players for boys and super online chat lovers for girls as a symbolic phrase in the current adolescent culture, this project of data analysis verifies the displacement effect on deteriorating mathematics performance. To evaluate correlation or regression coefficients between a factor of playing video games or chatting online and mathematics performance compared with other factors, we use multivariate analysis technique and take gender difference into account. We find the most important reason for the negative sign of the displacement effect on mathematics performance due to students’ poor academic background. Statistical analysis methods in this project could be applied to study internet users’ academic performance from the high school education to the college education.

Keywords: correlation coefficients, displacement effect, multivariate analysis technique, regression coefficients

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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: Shahadut Hossain, Jacek Wesolowski, Zahirul Hoque

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In many epidemiological and environmental studies covariate measurements are subject to the detection limit. In most applications, covariate measurements are usually truncated from below which is known as left-truncation. Because the measuring device, which we use to measure the covariate, fails to detect values falling below the certain threshold. In regression analyses, it causes inflated bias and inaccurate mean squared error (MSE) to the estimators. This paper suggests a response-based regression calibration method to correct the deleterious impact introduced by the covariate detection limit in the estimators of the parameters of simple logistic regression model. Compared to the maximum likelihood method, the proposed method is computationally simpler, and hence easier to implement. It is robust to the violation of distributional assumption about the covariate of interest. In producing correct inference, the performance of the proposed method compared to the other competing methods has been investigated through extensive simulations. A real-life application of the method is also shown using data from a population-based case-control study of non-Hodgkin lymphoma.

Keywords: environmental exposure, detection limit, left truncation, bias, ad-hoc substitution

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

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Authors: Sahar Elsaleh, Ghada Farhat, Shaikha Al-Derham, Fasih Alam

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Background: Cesarean section (CS) delivery is one of the major concerns both in developing and developed countries. The rate of CS deliveries are on the rise globally, and especially in Qatar. Many socio-economic, demographic, clinical and institutional factors play an important role for cesarean sections. This study aims to investigate factors affecting the prevalence of CS among women in Qatar using the UNICEF’s Multiple Indicator Cluster Survey (MICS) 2012 database. Methods: The study has focused on the women’s questionnaire of the MICS, which was successfully distributed to 5699 participants. Following study inclusion and exclusion criteria, a final sample of 761 women aged 19- 49 years who had at least one delivery of giving birth in their lifetime before the survey were included. A number of socio-economic, demographic, clinical and institutional factors, identified through literature review and available in the data, were considered for the analyses. Bivariate and multivariate logistic regression models, along with a multi-level modeling to investigate clustering effect, were undertaken to identify the factors that affect CS prevalence in Qatar. Results: From the bivariate analyses the study has shown that, a number of categorical factors are statistically significantly associated with the dependent variable (CS). When identifying the factors from a multivariate logistic regression, the study found that only three categorical factors -‘age of women’, ‘place at delivery’ and ‘baby weight’ appeared to be significantly affecting the CS among women in Qatar. Although the MICS dataset is based on a cluster survey, an exploratory multi-level analysis did not show any clustering effect, i.e. no significant variation in results at higher level (households), suggesting that all analyses at lower level (individual respondent) are valid without any significant bias in results. Conclusion: The study found a statistically significant association between the dependent variable (CS delivery) and age of women, frequency of TV watching, assistance at birth and place of birth. These results need to be interpreted cautiously; however, it can be used as evidence-base for further research on cesarean section delivery in Qatar.

Keywords: cesarean section, factors, multiple indicator cluster survey, MICS database, Qatar

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Authors: Iyanujesu Adereti, Tajudeen Basiru, Ayodamola Olanipekun

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Background: Substance abuse among adolescents is of public health concern globally. Despite being the most abused by adolescents, there are limited studies on the prevalence of alcohol use and cigarette smoking among adolescents in Mauritius. Objectives: To determine the prevalence of cigarette smoking, alcohol use and associated correlates among school-going adolescents in Mauritius. Methodology: Data obtained from 2017 WHO Global School-based Student Health Survey (GSHS) survey of 3,012 school-going adolescents in Mauritius was analyzed using STATA. Descriptive statistics were used to obtain prevalence. Bivariate and multivariate logistic regression analysis was used to evaluate predictors of cigarette smoking and alcohol use. Results: Prevalence of alcohol consumption and cigarette smoking were 26.0% and 17.1%, respectively. Smoking and alcohol use was more prevalent among males, younger adolescents, and those in higher school grades (p-value <.000). In multivariable logistic regression, male gender was associated with a higher risk of cigarette smoking (adjusted Odds Ratio (aOR) [95%Confidence Interval (CI)]= 1.51[1.06-2.14]) but lower risk of alcohol use (aOR[95%CI]= 0.69[0.53-0.90]) while older age (mid and late adolescence) and parental smoking were found to be associated with increased risk of alcohol use (aOR[95%CI]= 1.94[1.34-2.99] and 1.36[1.05-1.78] respectively). Marijuana use, truancy, being in a fight and suicide ideation were associated with increased odds of alcohol use (aOR[95%CI]= 3.82[3.39-6.09]; 2.15[1.62-2.87]; 1.83[1.34-2.49] and 1.93[1.38-2.69] respectively) and cigarette smoking (aOR[95%CI]= 17.28[10.4 - 28.51]; 1.73[1.21-2. 49]; 1.67[1.14-2.45] and 2.17[1.43-3.28] respectively) while involvement in sexual activity was associated with reduced risk of alcohol use (aOR[95%CI]= 0.50[0.37-0.68]) and cigarette smoking (aOR[95%CI]= 0.47[0.33-0.69]). Parental support and parental monitoring were uniquely associated with lower risk of cigarette smoking (aOR[95%CI]= 0.69[0.47-0.99] and 0.62[0.43-0.91] respectively). Conclusion: The high prevalence of alcohol use and cigarette smoking in this study shows the need for the government of Mauritius to enhance policies that will help address this issue putting into accounts the various risk and protective factors.

Keywords: adolescent health, alcohol use, cigarette smoking, global school-based student health survey

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Authors: Keiko Naito, Y. Nakashima, S. Yamauchi, Y. Kunitani, Y. Ishigami, K. Numata, M. Mizobe, Y. Homma, J. Takahashi, T. Inoue, T. Shiga, H. Funakoshi

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Background: A presence of severe acidemia, defined as pH < 7.2, is common during endotracheal intubation for critically ill patients in the emergency department (ED). Severe acidemia is widely recognized as a predisposing factor for intubation failure. However, it is unclear that acidemic condition itself actually makes endotracheal intubation more difficult. We aimed to evaluate if a presence of severe acidemia before intubation is associated with the lower first attempt intubation success rate in the ED. Methods: This is a retrospective observational cohort study in the ED of an urban hospital in Japan. The collected data included patient demographics, such as age, sex, and body mass index, presence of one or more factors of modified LEMON criteria for predicting difficult intubation, reasons for intubation, blood gas levels, airway equipment, intubation by emergency physician or not, and the use of the rapid sequence intubation technique. Those with any of the following were excluded from the analysis: (1) no blood gas drawn before intubation, (2) cardiopulmonary arrest, and (3) under 18 years of age. The primary outcome was the first attempt intubation success rates between a severe acidemic patients (SA) group and a non-severe acidemic patients (NA) group. Logistic regression analysis was used to test the first attempt success rates for intubations between those two groups. Results: Over 5 years, a total of 486 intubations were performed; 105 in the SA group and 381 in the NA group. The univariate analysis showed that the first attempt intubation success rate was lower in the SA group than in the NA group (71.4% vs 83.5%, p < 0.01). The multivariate logistic regression analysis identified that severe acidemia was significantly associated with the first attempt intubation failure (OR 1.9, 95% CI 1.03-3.68, p = 0.04). Conclusions: A presence of severe acidemia before endotracheal intubation lowers the first attempt intubation success rate in the ED.

Keywords: acidemia, airway management, endotracheal intubation, first-attempt intubation success rate

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Authors: C. M. Mat Isa, H. Mohd Saman, S. R. Mohd Nasir, A. Jaapar

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International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.

Keywords: factors, early movers, entry timing choices, late movers, logistic regression model, principal axis factorial analysis, Malaysian construction firms

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Authors: Jiaqian Yin, Ruoling Chen, David Churchill, Huijuan Zou, Peipei Guo, Chunmei Liang, Xiaoqing Peng, Zhikang Zhang, Weiju Zhou, Yunxia Cao

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Purpose: This study aimed to explore the interaction of male and female age on implantation failure from in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments in couples following their first cycles using the Anhui Maternal-Child Health Study (AMCHS). Methods: The AMCHS recruited 2042 infertile couples who were physically fit for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) treatment at the Reproductive Centre of the First Affiliated Hospital of Anhui Medical University between May 2017 to April 2021. This prospective cohort study analysed the data from 1910 cohort couples for the current paper data analysis. The multivariate logistic regression model was used to identify the effect of male and female age on implantation failure after controlling for confounding factors. Male age and female age were examined as continuous and categorical (male age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40; female age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40) predictors. Results: Logistic regression indicated that advanced maternal age was associated with increased implantation failure (P<0.001). There was evidence of an interaction between maternal age (30-<35 and ≥ 35) and paternal age (≥35) on implantation failure. (p<0.05). Only when the male was ≥35 years of increased maternal age was associated with the risk of implantation failure. Conclusion: In conclusion, there was an additive effect on implantation failure with advanced parental age. The impact of advanced maternal age was only seen in the older paternal age group. The delay of childbearing in both men and women will be a serious public issue that may contribute to a higher risk of implantation failure in patients needing assisted reproductive technology (ART).

Keywords: parental age, infertility, cohort study, IVF

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Authors: Paria Soleimani, Arezoo Neshati

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Myocardial infarction is one of the leading causes of ‎death in the world. Some of these deaths occur even before the patient ‎reaches the hospital. Myocardial infarction occurs as a result of ‎impaired blood supply. Because the most of these deaths are due to ‎coronary artery disease, hence the awareness of the warning signs of a ‎heart attack is essential. Some heart attacks are sudden and intense, but ‎most of them start slowly, with mild pain or discomfort, then early ‎detection and successful treatment of these symptoms is vital to save ‎them. Therefore, importance and usefulness of a system designing to ‎assist physicians in the early diagnosis of the acute heart attacks is ‎obvious.‎ The purpose of this study is to determine how well a predictive ‎model would perform based on the only patient-reportable clinical ‎history factors, without using diagnostic tests or physical exams. This ‎type of the prediction model might have application outside of the ‎hospital setting to give accurate advice to patients to influence them to ‎seek care in appropriate situations. For this purpose, the data were ‎collected on 711 heart patients in Iran hospitals. 28 attributes of clinical ‎factors can be reported by patients; were studied. Three logistic ‎regression models were made on the basis of the 28 features to predict ‎the risk of heart attacks. The best logistic regression model in terms of ‎performance had a C-index of 0.955 and with an accuracy of 94.9%. ‎The variables, severe chest pain, back pain, cold sweats, shortness of ‎breath, nausea, and vomiting were selected as the main features.‎

Keywords: Coronary heart disease, Acute heart attacks, Prediction, Logistic ‎regression‎

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Begum Rokeya, Rahelee Zinnat, Fatema Jebunnesa, Israt Ara Hossain, A. Rahman

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Background and Aims: Arsenic in drinking water is a global environmental health problem, and the exposure may increase dyslipidemia and cerebrovascular diseases mortalities, most likely through causing atherosclerosis. However, the mechanism of lipid metabolism, atherosclerosis formation, arsenic exposure and impact in pregnancy is still unclear. Recent epidemiological evidences indicate close association between inorganic arsenic exposure via drinking water and Dyslipidemia. However, the exact mechanism of this arsenic-mediated increase in atherosclerosis risk factors remains enigmatic. We explore the association of the effect of arsenic on serum lipid profile in pregnant subjects. Methods: A total 200 pregnant mother screened in this study from arsenic exposed area. Our study group included 100 exposed subjects were cases and 100 Non exposed healthy pregnant were controls requited by a cross-sectional study. Clinical and anthropometric measurements were done by standard techniques. Lipidemic status was assessed by enzymatic endpoint method. Urinary As was measured by inductively coupled plasma-mass spectrometry and adjusted with specific gravity and Arsenic exposure was assessed by the level of urinary arsenic level > 100 μg/L was categorized as arsenic exposed and < 100 μg/L were categorized as non-exposed. Multivariate logistic regression and Student’s t - test was used for statistical analysis. Results: Systolic and diastolic blood pressure both were significantly higher in the Arsenic exposed pregnant subjects compared to the Non-exposed group (p<0.001). Arsenic exposed subjects had 2 times higher chance of developing hypertensive pregnancy (Odds Ratio 2.2). In parallel to the findings in Ar exposed subjects showed significantly higher proportion of triglyceride and total cholesterol and low density of lipo protein when compare to non- arsenic exposed pregnant subjects. Significant correlation of urinary arsenic level was also found with SBP, DBP, TG, T chol and serum LDL-Cholesterol. On multivariate logistic regression showed urinary arsenic had a positive association with DBP, SBP, Triglyceride and LDL-c. Conclusion: In conclusion, arsenic exposure may induce dyslipidemia like atherosclerosis through modifying reverse cholesterol transport in cholesterol metabolism. For decreasing atherosclerosis related mortality associated with arsenic, preventing exposure from environmental sources in early life is an important element.

Keywords: Arsenic Exposure, Dyslipidemia, Gestational Diabetes Mellitus, Serum lipid profile

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Authors: Sandra Adarve, Jhon Osorio

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Uncertainty has been studied in patients with different types of cancer, except in patients with hematologic cancer and undergoing transplantation. The purpose of this study was to identify factors associated with uncertainty in adults patients with malignant hemato-oncology diseases who are scheduled to undergo hematopoietic stem cell transplantation based on Merle Mishel´s Uncertainty theory. This was a cross-sectional study with an analytical purpose. The study sample included 50 patients with leukemia, myeloma, and lymphoma selected by non-probability sampling by convenience and intention. Sociodemographic and clinical variables were measured. Mishel´s Scale of Uncertainty in Illness was used for the measurement of uncertainty. A bivariate and multivariate analyses were performed to explore the relationships and associations between the different variables and uncertainty level. For this analysis, the distribution of the uncertainty scale values was evaluated through the Shapiro-Wilk normality test to identify statistical tests to be used. A multivariate analysis was conducted through a logistic regression using step-by-step technique. Patients were 18-74 years old, with a mean age of 44.8. Over time, the disease course had a median of 9.5 months, an opportunity was found in the performance of the transplantation of < 20 days for 50% of the patients. Regarding the uncertainty scale, a mean score of 95.46 was identified. When the dimensions of the scale were analyzed, the mean score of the framework of stimuli was 25.6, of cognitive ability was 47.4 and structure providers was 22.8. Age was identified to correlate with the total uncertainty score (p=0.012). Additionally, a statistically significant difference was evidenced between different religious creeds and uncertainty score (p=0.023), education level (p=0.012), family history of cancer (p=0.001), the presence of comorbidities (p=0.023) and previous radiotherapy treatment (p=0.022). After performing logistic regression, previous radiotherapy treatment (OR=0.04 IC95% (0.004-0.48)) and family history of cancer (OR=30.7 IC95% (2.7-349)) were found to be factors associated with the high level of uncertainty. Uncertainty is present in high levels in patients who are going to be subjected to bone marrow transplantation, and it is the responsibility of the nurse to assess the levels of uncertainty and the presence of factors that may contribute to their presence. Once it has been valued, the uncertainty must be intervened from the identified associated factors, especially all those that have to do with the cognitive capacity. This implies the implementation and design of intervention strategies to improve the knowledge related to the disease and the therapeutic procedures to which the patients will be subjected. All interventions should favor the adaptation of these patients to their current experience and contribute to seeing uncertainty as an opportunity for growth and transcendence.

Keywords: hematopoietic stem cell transplantation, hematologic diseases, nursing, uncertainty

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Zunaidah Ab Hasan, Azhana Othman, Abd Halim Mohd Noor, Nor Shahrina Mohd Rafien

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Zakat is a portion of wealth contributed from financially able Muslims to be distributed to predetermine recipients; main among them are the poor and the needy. Distribution of the zakat fund is given with the objective to lift the recipients from poverty. Due to the multidimensional and multifaceted nature of poverty, it is imperative that the causes of poverty are properly identified for assistance given by zakat authorities reached the intended target. Despite, various studies undertaken to identify the poor correctly, there are reports of the poor not receiving the adequate assistance required from zakat. Thus, this study examines the determinants of poverty among applicants for zakat assistance distributed by the State Islamic Religious Council in Malacca (SIRCM). Malacca is a state in Malaysia. The respondents were based on the list of names of new zakat applicants for the month of April and May 2014 provided by SIRCM. A binary logistic regression was estimated based on this data with either zakat applications is rejected or accepted as the dependent variable and set of demographic variables and health as the explanatory variables. Overall, the logistic model successfully predicted factors of acceptance of zakat applications. Three independent variables namely gender, age; size of households and health significantly explain the likelihood of a successful zakat application. Among others, the finding suggests the importance of focusing on providing education opportunity in helping the poor.

Keywords: logistic regression, zakat distribution, status of zakat applications, poverty, education

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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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: Mohamed Elkoushy, Sameer Munshi, Waseem Tayeb

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Background: Flexible ureteroscopy (fURS) has dramatically improved the minimally invasive management of complex nephrolithiasis. fUR is increasingly being used as the first-line treatment for patients with renal stones. Stone-free status (SFS) is the primary goal in the management of patients with urolithiasis. However, substantial variations exist in the reported SFS following fURS. Objectives: This study determines the predictors of SFS after a single session of fURS with holmium laser lithotripsy (HLL) for renal calculi. Methods: A retrospective review of prospectively collected data was performed for all consecutive patients undergoing fURS and HLL for renal calculi at a tertiary care center. Patients with previous ipsilateral URS for the same stones were excluded. All patients underwent JJ ureteral stent insertion at the end of the procedure. SFS was defined as the presence of no residuals or ≤4-mm non-obstructing stone and was assessed by CT/KUB imaging after 3-4 weeks post-operatively. Multivariate logistic regression was used to detect possible predictors of SFS. Results: A total of 212 patients were included with a mean age of 52.3±8.3 years and a stone burden <20 mm (49.1%), 20-30 mm (41.0%) and >30 mm (9.9%). Overall SFS after a single session of fURS was 71.7%, 92% and 52% for stones less and larger than 20 mm, respectively. Patients with stones> 20 mm need retreatment with a mean number of 1.8 (1.3-2.7) fURS. SFS was significantly associated with male gender, stone bulk <20 mm (95.7% vs. 56.2%), non-lower pole stones, hydronephrotic kidney, low stone intensity, ureteral access sheath, and preoperative stenting. SFS was associated with a lower readmission rate (5.9% vs. 38.9%) and urinary tract infections (3.8% vs. 25.9%). In multivariate regression analysis, SFS maintains its significant association with low stone burden of <20 mm (OR: 5.21), stone intensity <600 HFU (OR: 2.87), and non-lower caliceal stones (OR: 3.84). Conclusion: Best results after a single-session fURS for renal stone were obtained for the stone burden of less than 20 mm and low stone attenuation. Lower calyceal stones may influence stone clearance and need a different approach than fURS, especially for higher stone burden.

Keywords: ureteroscopy, kidney stone, lithotripsy, stone-free, predictors

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Authors: Himansu, Bedanga Talukdar

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The aim of present study is to investigate the prevalence of various health outcomes and quality of life and analyzes the moderating role of social capital on health outcomes (i.e., self-rated good health (SRH), depression, functional health and quality of life) among elderly in India. Using WHO Study on Global AGEing and adults health (SAGE) data, with sample of 6559 elderly between 50 and above (Mage=61.81, SD=9.00) age were selected for analysis. Multivariate analysis accessed the prevalence of SRH, depression, functional limitation and quality of life among older adults. Logistic regression evaluates the effect of social capital along with other co-founders on SRH, depression, and functional limitation, whereas linear regression evaluates the effect of social capital with other co-founders on quality of life (QoL) among elderly. Empirical results reveal that (74%) of respondents were married, (70%) having low social action, (46%) medium sociability, (45%) low trust-solidarity, (58%) high safety, (65%) medium civic engagement and 37% reported medium psychological resources. The multivariate analysis, explains (SRH) is associated with age, female, having education, higher social action great trust, safety and greater psychological resources. Depression among elderly is greatly related to age, sex, education and higher wealth, higher sociability, having psychological resources. QoL is negatively associated with age, sex, being Muslim, whereas positive associated with higher education, currently married, civic engagement, having wealth, social action, trust and solidarity, safeness, and strong psychological resources.

Keywords: depressive symptom, functional limitation, older adults, quality of life, self rated health, social capital

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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)

Procedia PDF Downloads 357