Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

• Published in: 2010 International Conference on Computer Information Systems and Industrial Management Applications pp. 193 - 198
• Main Authors: Khemphila, A, Boonjing, V
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.10.2010
• Subjects: Artificial neural networks; Classification trees; Data mining; data mining techniques; Data models; Decision trees; Heart; heart disease; Logistic regression classifiers; Logistics; Regression tree analysis
• ISBN: 9781424478170; 1424478170
• DOI: 10.1109/CISIM.2010.5643666

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.