Risk Prediction Analysis of Cardiovascular Disease Using Supervised Machine Learning Techniques

Abstract

The best thing to avoid strategic human death rates due to curable diseases is to detect them early and prevent their onset. Presently, in our society, large numbers of death rates are due to cardiovascular disease (CVD). Hence early detection of CVD is critical even though many more practices exist for earlier prediction of risk. One approach for early disease risk prediction is the use of risk prediction models developed using machine learning techniques. These models will provide clinicians to treat heart disease of the patient in a better way. Consequently in this chapter, classification mechanisms have been applied to predict the status of the disease. The machine learning algorithms involved in the prediction of CVD are EDC-AIRS, Decision Tree, and SVM. The heart disease dataset from UCI repository has been used in this study. The predictions are denoted by means of accuracy, whereas the performance measures have been calculated in terms of sensitivity, specificity, and F-measure. Results indicate that the prediction model developed using the SVM algorithm is capable of achieving high sensitivity, specificity, balanced accuracy, and F-measure. Further, these models can be integrated into a computer-aided screening tool which clinicians can use to predict the risk status of CVD after performing the necessary clinical assessments.