Heart Stroke Prediction Using Machine Learning Models

Abstract

Healthcare field has a huge amount of data. To deal with those data, many techniques are used. Someone, somewhere in the world, suffers from a stroke. When someone experiences a stroke, quick medical care is critical. Heart stroke is the leading cause of death worldwide. Heart stroke is similar to heart attack which affects the blood vessels of the heart. Different features can be used to predict the heart stroke. In order to predict the heart stroke, an effective heart stroke prediction system (EHSPS) is developed using machine learning algorithms. The datasets used are classified in terms of 12 parameters like hypertension, heart disease, BMI, smoking status, etc. These are the inputs for machine learning algorithms which are used to predict the heart stroke. The project aims to build a machine learning model which predicts the heart stroke.