Comparative Study of Machine Learning Classification Techniques to Predict the Cardiovascular Diseases Using HRFLC

Due to life style changes more people are facing health issues even at the younger age and Cardiovascular diseases (CAD) was one of the major issues in it. The death rate due to heart disease is higher compared to other health issue. Early prediction of the heart disease (CAD) can help to control the death rate. By adopting to AI, the detection can be faster and accurate which will save the patient life. In this paper the important Features for predicting Heart disease are decided and the Features are applied to different classification model to find the best model out of it. Feature selection is decided based on HRFLC algorithm which is the hybrid algorithm using Random Forest classifier, ADABOOST algorithm and statistical technique Pearson Correlation coefficient. The feature selected based on HRFLC is applied to different Machine Learning models and best model is decided based on different Evaluation Metrices.