Boruta based feature selection model for heart disease prediction

In today’s time the rate of heart disease is increasing at a very fast pace and because of that it is becoming the reason for major cause of deaths worldwide. It is very important to give treatment for heart disease or predict any such disease beforehand but there are some medical centers where experts lack appropriate or fair expertise to diagnose and treat the patient on time. So often they assume their readings and as a result, poor outcome is shown which sometimes lead to death of the patient. This paper identifies the relevant attributes of heart diseases using Boruta, Lasso and Ridge feature selection method. It also presents valuable insight on effectiveness of various machine learning algorithms to predict heart disease. The feature selection method reduces number of features and at the same time maintaining comparable accuracy of the model. Experimental results demonstrate that Boruta feature selection with Random Forest classifier outperforms all the other state-of-art methods used in this study.