Heart disease prediction system using random forest

Abstract

The scope of Machine Learning algorithms are increasing in predicting various diseases. The nature of machine learning algorithm to think like a human being is making this concept so important and versatile. Here the challenge of increasing the accuracy of Heart disease prediction is taken upon. The non-linear tendency of the Cleveland heart disease dataset was exploited for applying Random Forest to get an accuracy of 85.81%. The method of predicting heart diseases using Random Forest with well-set attributes fetches us more accuracy. Random Forest was built by training 303 instances of data and authentication of accuracy was done using 10-fold cross validation. By the proposed algorithm for heart disease prediction, many lives could be saved in the future. © Springer Nature Singapore Pte Ltd. 2017.