Performance Analysis of Machine Learning Techniques for Predicting Cardiac Disease

Early detection of cardiac disease is one of the significant issue in healthcare industry and machine learning techniques are widely used by researchers for its early prediction. Therefore, the objective of this study is to find the performance analysis of different machine learning techniques used for cardiac disease prediction. The frequently used ML techniques decision tree(DT), Naïve Bayes (NB), k nearest neighbor (KNN), support vector machine (SVM), linear regression (LR) and multilayer perceptron (MLP) are evaluated at k-fold 5 and 10. For experimentation, the Cleveland, Hungarian, Switzerland and Va datasets from UCI repository are considered. Missing values in original datasets are filled with constant value. Accuracy, Misclassification rate, True Positive rate, True Negative Rate, False Positive rate, Precision, and F-Score are used for performance comparison. After experimental study, it is found that SVM and KNN classifiers gives the significant performance at k-fold 10.