Heart Disease Prediction Using Machine Learning Algorithms

Abstract

Heart disease is a major issue that has become increasingly prevalent. According to current statistics, heart disease claims the life of one person every minute. In the last several years, one of the hardest problems facing the medical field is predicting heart disease. Reducing the death rate can be achieved with early detection of cardiac disease. Machine learning is the most effective approach to forecasting heart disease. This paper aims to create a lightweight, straightforward solution to detecting cardiac disease using machine learning. Machine learning can aid in heart disease prediction. This study analyzes several machine learning algorithms and performance indicators. This study compares cardiac disease detection methods using a publicly available dataset from the UCI machine learning repository. There are other datasets accessible, including the Switzerland and Cleveland databases. Here the dataset contains 303 patient records and 18 characteristics. The analysis shows that out of six machine learning algorithms, the Random Forest algorithm gives the best result with 94.50%. Keywords- cardiac disease detection, datasets, heart disease prediction, Machine Learning, Random Forest algorithm.