What question did this study set out to answer?

The aim is to develop a web-based risk prediction system for cardiovascular disease using machine learning techniques.

May 7, 2026

Web-based Cardiovascular Disease Risk Prediction using Machine Learning

Key Points

The aim is to develop a web-based risk prediction system for cardiovascular disease using machine learning techniques.
Developed a web-based application with a user-friendly interface.
Utilized an XGBoost classifier trained on a cleaned Kaggle CVD dataset.
Incorporated eleven clinical and lifestyle features for risk assessment.
Deployed the model using Streamlit for accessibility.
Achieved accuracy of approximately 0.73.
Reached a ROC-AUC score of about 0.80.
Facilitated probability-based risk categorization for users.

Abstract

This paper presents a web-based cardiovascular disease (CVD) risk prediction system that combines supervised machine learning with an accessible user interface. An XGBoost classifier is trained on a cleaned Kaggle CVD dataset using eleven low-cost clinical and lifestyle features. The model achieves competitive performance (accuracy ~0.73, ROC-AUC ~ 0.80) and is deployed via a Streamlit application that provides probability-based risk categories for preliminary self-screening, illustrating an end-to- end pipeline from data preprocessing to cloud deployment.

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Web-based Cardiovascular Disease Risk Prediction using Machine Learning

Key Points

Abstract

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