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Author: search.filters.author.Boonjing, VeeraSubject: search.filters.subject.Artificial neural networks

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    Heart disease classification using neural network and feature selection
    (2011-11-04)
    Khemphila, Anchana
    ;
    Boonjing, Veera
    In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor's expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. Our work is to classify the presence of heart disease with reduced number of attributes. Original, 13 attributes are involved in classify the heart disease. We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients. The Artificial neural networks is used to classify the diagnosis of patients. Thirteen attributes are reduced to 8 attributes. The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%. © 2011 IEEE.
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    Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients
    (2010-12-01)
    Khemphila, Anchana
    ;
    Boonjing, Veera
    In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease.A retrospective analysis was performed in 303 subjects.We compared the performance of logistic regression(LR),decision trees(DTs) , and Artificial neural networks (ANNs).The variables were medical profiles are age,Sex,Chest Pain Type,Blood Pressure,Cholesterol,Fasting Blood Sugar, Resting ECG,Maximum Heart Rate,Induced Angina,Ole Peak,Slope,Number Colored Vessels,Thal and Concept Class.We have created the model using logistic regression classifiers , artificial neural networks and decision trees that they are often used for classification problems.Performances of classification techniques were compared using lift chart and error rates.In the result, artificial neural networks have the greatest area between the model curve and the baseline curve.The error rates are 0.22,0.198,0.21,respectively for logistic regression , artificial neural networks and decision trees.The neural networks exhibited sensitivity of 81.1% , specificity of 78.7% and accuracy of 80.2%,while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%,76.0% and 79.3%.And the logistic regression provided the prediction performance with a sensitivity,specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy ,therefore Artificial neural networks is the best technique to classify in this data set. ©2010 IEEE.