Heart Disease Prediction using Ensemble ML

Abstract

Worldwide, heart disease is one of the main causes of mortality. Heart disease early identification and prevention can significantly enhance patient outcomes and save healthcare expenditures. This research intends to create a model for forecasting an individual’s probability of developing heart disease based on several risk factors. A sizable patient data collection, comprising demographic information, laboratory test results, and lifestyle characteristics, will be used to train the algorithm. Standard measures like accuracy, sensitivity, and specificity will be used to assess the model’s performance. This project aims to develop a tool that will help medical practitioners recognize high-risk individuals and carry out early therapies to stop the progression of heart disease.