A Comparative Study on Decision Tree and Random Tree Approach in Predicting Heart Diseases

Abstract

Heart disease is one of the leading causes of death in the globe. All doctors cannot be equally proficient in every domain and well versed and skillfull doctors can’t be available at all time. An automated medical diagnosis system would improve medical treatment while simultaneously lowering expenses. Predicting the course of illness is a difficult endeavor. Data mining is used to infer diagnostic principles automatically and assist professionals in making the diagnosis process more trustworthy. Researchers employ a variety of data mining approaches to assist health care practitioners in predicting cardiac disease. A classification model would be a fit model for predicting diseases accurately. We present you with a comparison of two classification models, Decision trees and Random Forest Models. We find that Random Forest method performs better than Decision Tree model