Web-based Cardiovascular Disease Risk Prediction using Machine Learning

• Author(s): Arjun Singh; Arpita Yadav; Anjali Singh Yadav; Amit Kumar
• Paper ID: 1717397
• Page: 137-144
• Published Date: 05-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

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.

Citations

IRE Journals:
Arjun Singh, Arpita Yadav, Anjali Singh Yadav, Amit Kumar "Web-based Cardiovascular Disease Risk Prediction using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 137-144 https://doi.org/10.64388/IREV9I11-1717397

IEEE:
Arjun Singh, Arpita Yadav, Anjali Singh Yadav, Amit Kumar "Web-based Cardiovascular Disease Risk Prediction using Machine Learning" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717397