Search results for: ROC

Authors: Gary D. Cantrell, E. Alex Baylot

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The radius-of-curvature (ROC) defines the degree of curvature along the centerline of a roadway whereby a travelling vehicle must follow. Roadway designs must encompass ROC in mitigating the cost of earthwork associated with construction while also allowing vehicles to travel at maximum allowable design speeds. Thus, a road will tend to follow natural topography where possible, but curvature must also be optimized to permit fast, but safe vehicle speeds. The more severe the curvature of the road, the slower the permissible vehicle speed. For route planning, whether for urban settings, emergency operations, or even parcel delivery, ROC is a necessary attribute of road arcs for computing travel time. It is extremely rare for a geo-spatial database to contain ROC. This paper will present a procedure and mathematical algorithm to calculate and assign ROC to a segment pair and/or polyline.

Keywords: linear features, radius-of-curvature, roads, routing, traffic, turning

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Authors: J. S. Nah, A. Y. Jeon, J. H. Ro, G. R. Jeon

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ECG analysis method was developed using ROC analysis of PVC detecting algorithm. ECG signal of MIT-BIH arrhythmia database was analyzed by MATLAB. First of all, the baseline was removed by median filter to preprocess the ECG signal. R peaks were detected for ECG analysis method, and normal VCG was extracted for VCG analysis method. Four PVC detecting algorithm was analyzed by ROC curve, which parameters are maximum amplitude of QRS complex, width of QRS complex, r-r interval and geometric mean of VCG. To set cut-off value of parameters, ROC curve was estimated by true-positive rate (sensitivity) and false-positive rate. sensitivity and false negative rate (specificity) of ROC curve calculated, and ECG was analyzed using cut-off value which was estimated from ROC curve. As a result, PVC detecting algorithm of VCG geometric mean have high availability, and PVC could be detected more accurately with amplitude and width of QRS complex.

Keywords: Vectorcardiogram (VCG), Premature Ventricular contraction (PVC), ROC (receiver operating characteristic) curve, ECG

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Authors: O. Yavuz, L. Ozyilmaz

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HIV-1 genome is highly heterogeneous. Due to this variation, features of HIV-I genome is in a wide range. For this reason, the ability to infection of the virus changes depending on different chemokine receptors. From this point of view, R5 HIV viruses use CCR5 coreceptor while X4 viruses use CXCR5 and R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the experiments on HIV-1 genome. In this study, R5X4 type of HIV viruses were classified using Auto Regressive (AR) model through Artificial Neural Networks (ANNs). The statistical data of R5X4, R5 and X4 viruses was analyzed by using signal processing methods and ANNs. Accessible residues of these virus sequences were obtained and modeled by AR model since the dimension of residues is large and different from each other. Finally the pre-processed data was used to evolve various ANN structures for determining R5X4 viruses. Furthermore ROC analysis was applied to ANNs to show their real performances. The results indicate that R5X4 viruses successfully classified with high sensitivity and specificity values training and testing ROC analysis for RBF, which gives the best performance among ANN structures.

Keywords: Auto-Regressive Model, HIV, Neural Networks, ROC Analysis.

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Authors: S. Makal, L. Ozyilmaz, S. Palavaroglu

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Gene, principal unit of inheritance, is an ordered sequence of nucleotides. The genes of eukaryotic organisms include alternating segments of exons and introns. The region of Deoxyribonucleic acid (DNA) within a gene containing instructions for coding a protein is called exon. On the other hand, non-coding regions called introns are another part of DNA that regulates gene expression by removing from the messenger Ribonucleic acid (RNA) in a splicing process. This paper proposes to determine splice junctions that are exon-intron boundaries by analyzing DNA sequences. A splice junction can be either exon-intron (EI) or intron exon (IE). Because of the popularity and compatibility of the artificial neural network (ANN) in genetic fields; various ANN models are applied in this research. Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression Neural Networks (GRNN) are used to analyze and detect the splice junctions of gene sequences. 10-fold cross validation is used to demonstrate the accuracy of networks. The real performances of these networks are found by applying Receiver Operating Characteristic (ROC) analysis.

Keywords: Gene, neural networks, ROC analysis, splice junctions.

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Authors: Sepehr M.H.Jamarani, M.H.Moradi, H.Behnam, G.A.Rezai Rad

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This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and multiwaveletpacket based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands,, reconstructing the mammograms from the subbands containing only high frequencies. For this approach we employed different types of multiwaveletpacket. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve.

Keywords: Breast cancer, neural networks, diagnosis, multiwavelet packet, microcalcification.

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: Approach instance-based, area Under the ROC Curve, Patient-specific Decision Path, clinical predictions.

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Authors: Viet Dzung Nguyen, Duc Thuan Nguyen, Tien Dzung Nguyen, Van Thanh Pham

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Mammography is the most effective procedure for an early diagnosis of the breast cancer. Nowadays, people are trying to find a way or method to support as much as possible to the radiologists in diagnosis process. The most popular way is now being developed is using Computer-Aided Detection (CAD) system to process the digital mammograms and prompt the suspicious region to radiologist. In this paper, an automated CAD system for detection and classification of massive lesions in mammographic images is presented. The system consists of three processing steps: Regions-Of- Interest detection, feature extraction and classification. Our CAD system was evaluated on Mini-MIAS database consisting 322 digitalized mammograms. The CAD system-s performance is evaluated using Receiver Operating Characteristics (ROC) and Freeresponse ROC (FROC) curves. The archived results are 3.47 false positives per image (FPpI) and sensitivity of 85%.

Keywords: classification, computer-aided detection, featureextraction, mass detection.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

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The protein domain structure has been widely used as the most informative sequence feature to computationally predict protein-protein interactions. However, in a recent study, a research group has reported a very high accuracy of 94% using hydrophobicity feature. Therefore, in this study we compare and verify the usefulness of protein domain structure and hydrophobicity properties as the sequence features. Using the Support Vector Machines (SVM) as the learning system, our results indicate that both features achieved accuracy of nearly 80%. Furthermore, domains structure had receiver operating characteristic (ROC) score of 0.8480 with running time of 34 seconds, while hydrophobicity had ROC score of 0.8159 with running time of 20,571 seconds (5.7 hours). These results indicate that protein-protein interaction can be predicted from domain structure with reliable accuracy and acceptable running time.

Keywords: Bioinformatics, protein-protein interactions, support vector machines, protein features.

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Authors: U. Bottigli, D.Cascio, F. Fauci, B. Golosio, R. Magro, G.L. Masala, P. Oliva, G. Raso, S.Stumbo

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Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network.

Keywords: Neural Networks, K-Nearest Neighbours, SupportVector Machine, Computer Aided Diagnosis.

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Authors: Zeyneb Kurt, Oguzhan Yavuz

Abstract:

The feature of HIV genome is in a wide range because of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point, R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the coreceptors of HIV genome. The aim of this study is to develop the optimal Multilayer Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model (AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4 viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.

Keywords: Multilayer Perceptron, Auto-Regressive Model, HIV, ROC Analysis

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Authors: U. Bottigli, R.Chiarucci, B. Golosio, G.L. Masala, P. Oliva, S.Stumbo, D.Cascio, F. Fauci, M. Glorioso, M. Iacomi, R. Magro, G. Raso

Abstract:

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

Keywords: Neural Networks, K-Nearest Neighbours, Support Vector Machine, Computer Aided Detection

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Authors: Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag

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Term Extraction, a key data preparation step in Text Mining, extracts the terms, i.e. relevant collocation of words, attached to specific concepts (e.g. genetic-algorithms and decisiontrees are terms associated to the concept “Machine Learning" ). In this paper, the task of extracting interesting collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as interesting/not interesting. From these examples, the ROGER algorithm learns a numerical function, inducing some ranking on the collocations. This ranking is optimized using genetic algorithms, maximizing the trade-off between the false positive and true positive rates (Area Under the ROC curve). This approach uses a particular representation for the word collocations, namely the vector of values corresponding to the standard statistical interestingness measures attached to this collocation. As this representation is general (over corpora and natural languages), generality tests were performed by experimenting the ranking function learned from an English corpus in Biology, onto a French corpus of Curriculum Vitae, and vice versa, showing a good robustness of the approaches compared to the state-of-the-art Support Vector Machine (SVM).

Keywords: Text-mining, Terminology Extraction, Evolutionary algorithm, ROC Curve.

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: Stacking, multi-layers, ensemble, multi-class.

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Authors: Hsing-Kuo Pao, John Case

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Biological sequences from different species are called or-thologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences -in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In prac-tice one can approximate it by computable compression methods. How-ever, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new ap-proach to overcome the problem that compression approximations may notwork well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empir-ical work described shows the new set of entropy-based machine learning attributes provides good separation between positive (ortholog) and nega-tive (non-ortholog) data - better than with good, previously known alter-natives (which do not employ some means to handle short sequences well).Also empirically compared are the new entropy based attribute set and a number of other, more standard similarity attributes sets commonly used in genomic analysis. The various similarity attributes are evaluated by cross validation, through boosted decision tree induction C5.0, and by Receiver Operating Characteristic (ROC) analysis. The results point to the conclu-sion: the new, entropy based attribute set by itself is not the one giving the best prediction; however, it is the best attribute set for use in improving the other, standard attribute sets when conjoined with them.

Keywords: compression, decision tree, entropy, ortholog, ROC.

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial part of maintaining a customer-oriented business in the telecommunications industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years, which has made it more important to understand customers’ needs in this strong market. For those who are looking to turn over their service providers, understanding their needs is especially important. Predictive churn is now a mandatory requirement for retaining customers in the telecommunications industry. Machine learning can be used to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: Machine Learning, Gradient Boosting, Logistic Regression, Churn, Random Forest, Decision Tree, ROC, AUC, F1-score.

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Authors: Kay-Anne Haykal, Issack Biyong

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The post-traumatic stress disorder (PTSD) rises from exposure to a traumatic event and appears by a persistent experience of this event. Several psychiatric co-morbidities are associated with PTSD and include mood disorders, anxiety disorders, and substance abuse. The main objective was to compare the criteria for PTSD according to the literature to those used to diagnose a patient in a francophone hospital and to check the correspondence of these two criteria. 700 medical charts of admitted patients on the medicine or psychiatric unit at the Montfort Hospital were identified with the following diagnoses: major depressive disorder, bipolar disorder, anxiety disorder, substance abuse, and PTSD for the period of time between April 2005 and March 2006. Multiple demographic criteria were assembled. Also, for every chart analyzed, the PTSD criteria, according to the Manual of Mental Disorders (DSM) IV were found, identified, and grouped according to pre-established codes. An analysis using the receiver operating characteristic (ROC) method was elaborated for the study of data. A sample of 57 women and 50 men was studied. Age was varying between 18 and 88 years with a median age of 48. According to the PTSD criteria in the DSM IV, 12 patients should have the diagnosis of PTSD in opposition to only two identified in the medical charts. The ROC method establishes that with the combination of data from PTSD and depression, the sensitivity varies between 0,127 and 0,282, and the specificity varies between 0,889 and 0,917. Otherwise, if we examine the PTSD data alone, the sensibility jumps to 0.50, and the specificity varies between 0,781 and 0,895. This study confirms the presence of an underdiagnosed and treated PTSD that causes severe perturbations for the affected individual.

Keywords: Post-Traumatic Stress Disorder, diagnosis, co-morbidities, mental health disorders.

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Authors: Laureano Rodríguez, Gladys Casas, Ricardo Grau, Yailen Martínez

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The classical temporal scan statistic is often used to identify disease clusters. In recent years, this method has become as a very popular technique and its field of application has been notably increased. Many bioinformatic problems have been solved with this technique. In this paper a new scan fuzzy method is proposed. The behaviors of classic and fuzzy scan techniques are studied with simulated data. ROC curves are calculated, being demonstrated the superiority of the fuzzy scan technique.

Keywords: Scan statistic, fuzzy scan, simulating study

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Authors: Sangjun Park, Jungpyo Hong, Byung-Ok Kang, Yun-keun Lee, Minsoo Hahn

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In this paper, an algorithm for detecting and attenuating puff noises frequently generated under the mobile environment is proposed. As a baseline system, puff detection system is designed based on Gaussian Mixture Model (GMM), and 39th Mel Frequency Cepstral Coefficient (MFCC) is extracted as feature parameters. To improve the detection performance, effective acoustic features for puff detection are proposed. In addition, detected puff intervals are attenuated by high-pass filtering. The speech recognition rate was measured for evaluation and confusion matrix and ROC curve are used to confirm the validity of the proposed system.

Keywords: Gaussian mixture model, puff detection and cancellation, speech enhancement.

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Authors: Aníbal Rodríguez Fuentes, Juan V. Lorenzo Ginori, Ricardo Grau Ábalo

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Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.

Keywords: Bioinformatics, Coding region prediction, Computational load reduction, Digital Signal Processing, Fourier Transform.

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Authors: Jianhua Wang, Yan Yu

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This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.

Keywords: RBF, Elman, anomaly detection, misuse detection, hybrid neural network.

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Authors: Sangjun Park, Sangbae Jeong, Moonsung Han, Minsoo hahn

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In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. The algorithm detects the DOAs of multiple sources using the inverse of the eigenvalue-weighted eigen spectra. To apply the algorithm to target signal detection for GSC-based beamforming, we utilize its spectral response for the target DOA in noisy conditions. For evaluation of the algorithm, the performance of the proposed target signal detection method is compared with that of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics(ROC) curves.

Keywords: Beamforming, direction of arrival, multiple signal classification, target signal detection.

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Authors: Suresh S. Salankar, Balasaheb M. Patre

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Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.

Keywords: Classification, MLP NN, backpropagation algorithm, SVM, Receiver Operating Characteristics.

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Authors: Moushira Zaki, Wafaa Ezzat, Yasser Elhosary, Omnia Saleh

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Nonalcoholic fatty liver disease (NAFLD) has increased in conjunction with obesity. The accuracy of risk factors for detecting NAFLD in obese adolescents has not undergone a formal evaluation. The aim of this study was to evaluate predictors of NAFLD among Egyptian female obese adolescents. The study included 162 obese female adolescents. All were subjected to anthropometry, biochemical analysis and abdominal ultrasongraphic assessment. Metabolic syndrome (MS) was diagnosed according to the IDF criteria. Significant association between presence of MS and NAFLD was observed. Obese adolescents with NAFLD had significantly higher levels of ALT, triglycerides, fasting glucose, insulin, blood pressure and HOMA-IR, whereas decreased HDL-C levels as compared with obese cases without NAFLD. Receiver– operating characteristic (ROC) curve analysis shows that ALT is a sensitive predictor for NAFLD, confirming that ALT can be used as a marker of NAFLD.

Keywords: Adolescents, Egyptians, obesity.

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Authors: Benjamin Anderson-Sackaney, Amr Abdel-Dayem

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This paper presents a comprehensive survey of recent source camera identification (SCI) systems. Then, the performance of various sensor pattern noise (SPN) estimators was experimentally assessed, under common photo response non-uniformity (PRNU) frameworks. The experiments used 1350 natural and 900 flat-field images, captured by 18 individual cameras. 12 different experiments, grouped into three sets, were conducted. The results were analyzed using the receiver operator characteristic (ROC) curves. The experimental results demonstrated that combining the basic SPN estimator with a wavelet-based filtering scheme provides promising results. However, the phase SPN estimator fits better with both patch-based (BM3D) and anisotropic diffusion (AD) filtering schemes.

Keywords: Sensor pattern noise, source camera identification, photo response non-uniformity, anisotropic diffusion, peak to correlation energy ratio.

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Authors: P. Prakasam, M. Madheswaran

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A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

Keywords: Bit Error rate, Receiver Operating Characteristics, Software Defined Radio, Wavelet Transform.

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Authors: Tessy Badriyah, Jim S. Briggs, Dave R. Prytherch

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It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.

Keywords: Decision Trees, Logistic Regression, clinical outcome, risk of mortality.

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders are becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic Generative Adversarial Networks (GAN) such as the Gaussian copula. We present the results from two synthetic data sets Conditional Tabular Generative Adversarial Network (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the Receiver Operating Characteristic Curve ROC and Area Under the curve AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: Synthetic data generation, Generative Adversarial Networks, GANs, Conditional Tabular GAN, CTGAN, Gaussian copula.

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Authors: Chang, Chun-Lang, Liu, Chun-Kai

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This study, for its research subjects, uses patients who had undergone total knee replacement surgery from the database of the National Health Insurance Administration. Through the review of literatures and the interviews with physicians, important factors are selected after careful screening. Then using Cross Entropy Method, Genetic Algorithm Logistic Regression, and Particle Swarm Optimization, the weight of each factor is calculated and obtained. In the meantime, Excel VBA and Case Based Reasoning are combined and adopted to evaluate the system. Results show no significant difference found through Genetic Algorithm Logistic Regression and Particle Swarm Optimization with over 97% accuracy in both methods. Both ROC areas are above 0.87. This study can provide critical reference to medical personnel as clinical assessment to effectively enhance medical care quality and efficiency, prevent unnecessary waste, and provide practical advantages to resource allocation to medical institutes.

Keywords: Total knee replacement, Case Based Reasoning, Cross Entropy Method, Genetic Algorithm Logistic Regression, Particle Swarm Optimization.

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Authors: Rahil Garnavi, Mohammad Aldeen, M. Emre Celebi, Alauddin Bhuiyan, Constantinos Dolianitis, George Varigos

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Automatic segmentation of skin lesions is the first step towards the automated analysis of malignant melanoma. Although numerous segmentation methods have been developed, few studies have focused on determining the most effective color space for melanoma application. This paper proposes an automatic segmentation algorithm based on color space analysis and clustering-based histogram thresholding, a process which is able to determine the optimal color channel for detecting the borders in dermoscopy images. The algorithm is tested on a set of 30 high resolution dermoscopy images. A comprehensive evaluation of the results is provided, where borders manually drawn by four dermatologists, are compared to automated borders detected by the proposed algorithm, applying three previously used metrics of accuracy, sensitivity, and specificity and a new metric of similarity. By performing ROC analysis and ranking the metrics, it is demonstrated that the best results are obtained with the X and XoYoR color channels, resulting in an accuracy of approximately 97%. The proposed method is also compared with two state-of-theart skin lesion segmentation methods.

Keywords: Border detection, Color space analysis, Dermoscopy, Histogram thresholding, Melanoma, Segmentation.

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

Abstract:

Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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