Development and Evaluation of a collection of Heart Disease Prediction Models using Decision Tree Algorithm based on the various Feature Selection Methods

Cardiovascular disease is considered one of the largest fatal diseases in the world. Heart diseases have surpassed more than 41% contribution to the mortality rate of human race. Grave concern is needed by the world community predominantly the medical science domain, to curb these fatal diseases before to enter into the unrestrained zone. So well planned and vigilant steps are needed to shrink the effects of these cardiovascular lethal diseases. In this research paper various heart disease prediction models are developed using decision tree algorithm based upon the four feature selection methods. Feature selection methods selects and ranks the high profile attributes to form effective prediction models. A comparative study is performed to analyze and select the high performance model based on the particular feature selection method.