An Effective Analytics and Performance Measurement of Different Machine Learning Algorithms for Predicting Heart Disease

Abstract

This Heart disease means any condition that affects to directly heart. Globally, Heart disease is the main reason for death. According to a survey, approximately 17.9 million people died from heart disease in 2019 (representing 32 percent of global deaths). The number of people dying is increasing at an alarming rate every day. So it is necessary to detect and prevent heart disease as soon as possible. Medical experts who work inside the field of coronary heart sickness can predict the rate of coronary heart disorder up to 69%, which is not so useful. Because of the invention of various machine learning techniques, intelligent machines can predict the chance of heart disease up to 84%, which will be helpful to prevent heart disease earlier. In this paper, for picking essential characteristics among all features in the dataset, the univariate feature selection approach was employed. One-of-a-kind machine learning algorithms like K-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine were used to assess the performance of these algorithms and forecast which one performs best. These machine learning approaches require less time to predict disease with more precision, resulting in the loss of valued lives all around the world.