Open Access

ARTICLE

A Federated Learning Approach for Cardiovascular Health Analysis and Detection

Farhan Sarwar¹, Muhammad Shoaib Farooq¹, Nagwan Abdel Samee^2,*, Mona M. Jamjoom³, Imran Ashraf^4,*

1 Department of Computer Science, School of System and Technology, University of Management and Technology, Lahore, 54000, Pakistan
2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea

* Corresponding Authors: Nagwan Abdel Samee. Email: ; Imran Ashraf. Email:

Computers, Materials & Continua 2025, 84(3), 5897-5914. https://doi.org/10.32604/cmc.2025.063832

Received 25 January 2025; Accepted 11 June 2025; Issue published 30 July 2025

Abstract

Environmental transition can potentially influence cardiovascular health. Investigating the relationship between such transition and heart disease has important applications. This study uses federated learning (FL) in this context and investigates the link between climate change and heart disease. The dataset containing environmental, meteorological, and health-related factors like blood sugar, cholesterol, maximum heart rate, fasting ECG, etc., is used with machine learning models to identify hidden patterns and relationships. Algorithms such as federated learning, XGBoost, random forest, support vector classifier, extra tree classifier, k-nearest neighbor, and logistic regression are used. A framework for diagnosing heart disease is designed using FL along with other models. Experiments involve discriminating healthy subjects from those who are heart patients and obtain an accuracy of 94.03%. The proposed FL-based framework proves to be superior to existing techniques in terms of usability, dependability, and accuracy. This study paves the way for screening people for early heart disease detection and continuous monitoring in telemedicine and remote care. Personalized treatment can also be planned with customized therapies.

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Keywords

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