Predictive Model for Heart-Related Issues Based on Demographic, Societal, and Lifestyle Factors

Bindu Chandra Shekar Reddy, Pravallika Dharmavarapu, Roopal Dixit, Prudhvi Kodali, Akanksha Ojha, Bonaventure Chidube Molokwu

2025

Abstract

This research predicts cardiovascular disease (CVD) risk by analyzing demographic, societal, and lifestyle factors, supporting early intervention for conditions like heart attacks. With CVD causing around 17.9 million deaths annually worldwide (WHO), there is a critical need for accessible, accurate predictive models. We propose an XGBoost-based machine learning model trained on a 70,000-patient dataset enriched with features such as median income, stress, and diet risk. After robust preprocessing and feature engineering-including BMI and pulse pressure-the model achieves 73% accuracy, 76% precision, 68% recall, 72% F1-score, and 80% ROC-AUC. Key predictors include pulse pressure, cholesterol, and age, indicating that this multifactor approach can enhance clinical decision-making and inform scalable health solutions.

Paper Citation

in Harvard Style

Reddy B., Dharmavarapu P., Dixit R., Kodali P., Ojha A. and Molokwu B. (2025). Predictive Model for Heart-Related Issues Based on Demographic, Societal, and Lifestyle Factors. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 358-365. DOI: 10.5220/0013692400003985

in Bibtex Style

@conference{webist25,
author={Bindu Reddy and Pravallika Dharmavarapu and Roopal Dixit and Prudhvi Kodali and Akanksha Ojha and Bonaventure Molokwu},
title={Predictive Model for Heart-Related Issues Based on Demographic, Societal, and Lifestyle Factors},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={358-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013692400003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Predictive Model for Heart-Related Issues Based on Demographic, Societal, and Lifestyle Factors
SN - 978-989-758-772-6
AU - Reddy B.
AU - Dharmavarapu P.
AU - Dixit R.
AU - Kodali P.
AU - Ojha A.
AU - Molokwu B.
PY - 2025
SP - 358
EP - 365
DO - 10.5220/0013692400003985
PB - SciTePress