AI-Based Predictive Tools for Managing and Preventing Cardiovascular Diseases

Saad Rasool; Abdullah Mazharuddin Khaja; Yawar Hayat; Arbaz Haider Khan

doi:10.55524/ijircst.2025.13.3.13

Abstract

A large percentage of deaths each year are caused by cardiovascular diseases (CVDs), which are a serious worldwide health concern. Particularly in varied groups with complicated risk profiles, the predictive value of current risk assessment techniques, such the Framingham Risk Score, is limited. In this work, we investigate how ML techniques can improve the prediction of cardiovascular risk by examining patterns that conventional models frequently overlook. Using a generated dataset designed to mimic real patient data, we applied and assessed a range of machine learning algorithms, such as logistic regression, support vector machines, random forests, XGBoost, and neural networks on a generated dataset designed to replicate actual patient information. Accuracy, sensitivity, specificity, and AUC metrics were used to evaluate each model. Our results demonstrate that ensemble approaches and neural networks perform better than traditional models, especially when it comes to identifying high-risk instances. The study takes into account how these tools could be ethically incorporated into healthcare settings in addition to their predictive power. We talk about issues with ethical use, data quality, and generalizability. All things considered, this work bolsters the expanding importance of AI in improving the efficiency, preventiveness, and personalization of cardiovascular care.

Keywords

Cardiovascular Diseases, Machine Learning, Artificial Intelligence, Risk Prediction, Personalized Medicine

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Cites this article as

S. Rasool, A. M. Khaja, Y. Hayat, A. H. Khan, "AI-Based Predictive Tools for Managing and Preventing Cardiovascular Diseases", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-13, Issue-3, Page No-76-81, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.13

Corresponding Author

Saad Rasool

AI Healthcare Researcher, Department of Computer Science and Engineering , American National University, Virginia, USA

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