Prediction of Heart Diseases in Individuals using Machine Learning Algorithm

Abstract

Heart Disease is such a common disease we hear about daily. But have you ever imagined heart diseases such as coronary heart disease, heart attack, congestive heart failure, and congenital heart disease as the leading cause of death in the USA? Every 40 seconds, someone has a heart attack, and every 36 seconds, someone is dying due to cardiovascular disease in the United States. Nearly 18.6 million people died of cardiovascular disease in 2019 globally (Gutierrez,2021). The heart is an engine of the human body which pumps blood around the body 24hrs. Therefore, it is worthwhile to invest time and research to improve our ability to diagnose heart disease early. Thankfully, knowing what drives heart disease and early detection can help in diminishing the heart disease rates. The purpose of this research is to conduct a step-by-step analysis of the data set (available on Kaggle) named ”Heart Disease UCI dataset” by examining factors and features that were common among people who have heart diseases. And using machine learning techniques, architectures, and models for early prediction.