What question did this study set out to answer?

The research aims to develop a predictive system for assessing heart disease risk using machine learning techniques.

April 22, 2026Open Access

Heart Disease Prediction System Using Machine Learning and Different Classification Techniques Like KNN and Naive Bayes

Key Points

The research aims to develop a predictive system for assessing heart disease risk using machine learning techniques.
Utilized KNN and Naive Bayes algorithms for prediction.
Developed a web-based platform with a user-friendly interface for data input.
Integrated technologies include React, Node.js, Express.js, Flask, and MongoDB.
The system provides real-time predictions based on user-inputted clinical parameters.
Users can assess their risk factors for heart disease timely.
Supports better healthcare decision-making through early detection.

Abstract

The Heart Disease Prediction System is a web-based application that predicts the risk of heart disease using machine learning algorithms such as KNN and Naive Bayes. It allows users to input clinical parameters like age, blood pressure, cholesterol, and other health factors. The system processes this data and provides real-time prediction results through an interactive interface. It is developed using React for frontend, Node.js and Express.js for backend, Flask for model integration, and MongoDB for data storage. The system aims to assist in early detection of heart disease and support better healthcare decisions.

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