Heart Disease Prediction using Machine Learning

Abstract

Abstract: The term "heart disease" refers to any heart disease or condition that can cause heart problems. Cardiovascular disease (CVD) is the leading cause of death worldwide, taking many lives each year. CVD is a group of cardiovascular diseases and includes heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. According to the World Health Organization (WHO), more than 17.9 million people worldwide die each year from coronary heart disease. If we take the example of India, every year the number of deaths due to heart disease has increased. Studies show that, from 2014 to 2019 the number of deaths from heart disease increased by 53%. Many threatening factors such as personal and work habits and genetic predisposition are major causes of heart disease. A variety of harmful habits such as smoking, alcohol and caffeine overdose, stress, and inactivity as well as other physical factors such as obesity, high blood pressure, high blood cholesterol, and pre-existing heart conditions are the main causes of heart disease. Over time, these harmful substances cause changes in the heart and blood vessels that can lead to heart attacks and strokes. Therefore, prevention of heart disease is very important to prevent these dangerous events and other potential complications of heart disease. Machine learning is a flexible part of AI that helps predict heart disease. In this research work, we will use the UCI database with 14 attributes to predict heart disease. The main goal of this study is to use ML algorithms to improve the heart disease prediction system and to more accurately predict these diseases in patients, thereby reducing the number of deaths by alerting patients. Keywords: Heart Diseases, Classification Algorithms, Machine Learning, UCI dataset.