Comparative analysis of various regularization techniques in the prediction of heart diseases

In the recent past it has been statistically proven that heart diseases are the largest contributing factor to the increase in mortality rate. Diagnosis of heart disease depends on a number of factors such as heart-rate, cholesterol levels, blood pressure, age and many other physiological factors. In most of the cases the diagnosis is done based on the experience and intuition of the doctor. This requires the doctor to be highly skilled and experienced in order to diagnose properly. It would be very helpful if we developed methods that could extract useful information from tonnes of clinical data. This paper reviews the L1/2 Logistic Regularization, Lasso, Elastic Net and Group Lasso regularization techniques, in order to select a subset of important features having the highest informative value in the prediction of heart diseases. Different regularization techniques select different subsets of features. This paper compares these techniques and finds out which is the most suitable method in the prediction of heart diseases.