Prediction of Heart Attack Using Machine Learning

Abstract

Cardiovascular diseases are one of the  biggest reasons for death of millions of people  around the world only second to cancer. A heart  attack occurs when a blood clot blocks the blood flow  to a part of the heart. In case this blood clot cuts off  the blood flow entirely, the part of the heart muscle  begins to die as a result. Going by the statistics, a  heart problem can gradually start between the age of  40-50 for people with unhealthy diet and bad lifestyle  choices. So, an early prognosis can really make a  huge difference in their lives by motivating them  towards a healthy and active life. By changing their  lifestyle and diet this risk can be controlled. This  Project intends to pinpoint the most relevant/risk  factors of heart disease as well as predict the overall  risk using machine learning. The machine learning  model predicts the likelihood of patients getting a  heart disease trained on dataset of other individuals.  As the result, the probability of getting a heart  disease based on current lifestyle and diet is  calculated. The model was trained with Framingham  heart study dataset.