Machine Learning Algorithms for Heart Disease Prediction

Abstract

Cardiovascular disease, otherwise known as heart disease, encompasses many diseases that affect the heart. Heart disease prediction is among the most complicated tasks in medical field. In the modern age, about one person dies every minute as a result of heart disease. In addition to many factors that contribute to heart disease, it's necessary at this point in time to acquire accurate, reliable, and sensible approaches to make an early diagnosis so that the disease may be managed appropriately. Due to the complexity of finding out the heart condition, the prediction process must be automated to avoid risks related to it and to alert the patient at an early stage. In the healthcare domain, data mining is commonly used to analyze huge, complex medical data and predict heart disease. Researchers apply a variety of data mining and machine learning approaches to analyse huge complex medical data and predict heart disease. In this study, various heart disease attribute are presented, and model is developed on the basis of supervised learning algorithm as K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, SVM, Light GBM and Naïve bayes. This Paper makes use of heart condition dataset available in Kaggle repository. The purpose of this study is to anticipate heart disease risk in patients. The results show that K-nearest neighbor provides the most accurate result.