Random Forest Based Heart Disease Prediction

Adeen, Preeti Sondhi

Abstract: The key explanation for a large number of deaths in the world over the last few decades is heart-related diseases or cardiovascular diseases (CVDs), which have emerged as the most life-threatening disease not just in India, but in the world as a whole. So, in order to identify such diseases in time for proper care, there is a need for a reliable, precise and feasible method. Algorithms and methods of machine learning have been applied to large data sets in the field of medicine for data processing. Several data mining and machine learning techniques are used by researchers to analyse vast data sets and assist in the accurate prediction of heart diseases. This paper analyses the Naïve Bayes, Help Vector Machine, Random Forest, supervised learning models to present a comparative analysis for the most effective algorithm. Random Forest has been found to have 95.08 % more precision compared to other algorithms.

Keywords: Heart diseases, Naïve Bayes, Support Vector Machine, Random Forest, Heart Disease Prediction