Analysis for Determining Best Machine learning Algorithm for Classification of Heart Diseases

Abstract

Numerous data points are generated by the healthcare sector and processed using certain procedures. There are many methods for processing a data among which data mining is one of the methods frequently employed. Heart condition is the main cause of death in the globe. This project determines the best algorithm for the system that anticipates the possibility of cardiac disease. The outcomes of this system provide the likelihood in percentage of acquiring heart disease. The datasets are categorised using medical parameters. To analyse such factors, our system employs a data mining classification method. The datasets are analysed using Naïve Bayes, Logistic Regression, Random Forest, K-Nearest Neighbour, XGboost, Decision Tree and Support Vector Machine, Machine learning algorithms with hybrid Classifiers and Neural Network.