Heart Risk Predictor
Year: 2025
Type: article
Abstract: -Heart disease is one of the leading causes of death globally. Predicting cardiovascular risk in the early stages can significantly reduce mortality rates and improve patient out-comes. This paper presents a machine learning-based solution for predicting heart disease risk using features such as age, cholesterol, blood pressure, smoking status, and diabetes. The system employs Linear Regression and Multivariable Polynomial Regression models trained on a dataset of 6,644 instances. A web interface built using Flask allows users to input health parameters and receive a risk score. The multivariable polynomial regression model achieved an accuracy of 75.8%. The paper also presents a comparative literature review of seven studies in heart disease prediction and discusses the implementation, sample circuit diagrams, code. and results.
Author: Dileep Singh , Ishuprabhakar , Gagan Sen , Hariom gujjar , Sudeep gujjar , Mr Abhishek Malviya
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7081570911
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