Federated and ensemble learning framework with optimized feature selection for heart disease detection

Olfa Hrizi; Karim Gasmi; Abdulrahman Alyami; Adel Alkhalil; Ibrahim Alrashdi; Ali Alqazzaz; Lassaad Ben Ammar; Manel Mrabet; Alameen E.M. Abdalrahman; Samia Yahyaoui; Olfa Hrizi; Karim Gasmi; Abdulrahman Alyami; Adel Alkhalil; Ibrahim Alrashdi; Ali Alqazzaz; Lassaad Ben Ammar; Manel Mrabet; Alameen E.M. Abdalrahman; Samia Yahyaoui

doi:10.3934/math.2025334

AIMS Mathematics

2025, Volume 10, Issue 3: 7290-7318. doi: 10.3934/math.2025334

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Federated and ensemble learning framework with optimized feature selection for heart disease detection

1.
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
2.
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
3.
Department of Software Engineering, College of Computer Science and Engineering, University of Hail, Hail, 81481 Saudi Arabia
4.
College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
5.
Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
6.
Department of Physics, College of Science, Jouf University, Sakaka, Aljouf 72341, Saudi Arabia

Received: 08 February 2025 Revised: 17 March 2025 Accepted: 24 March 2025 Published: 28 March 2025
MSC : 62H30, 68T05, 92C50, 68U35, 90C59

Predictive models for early identification of heart disease must be precise and efficient because it is a major worldwide health concern. To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. In addition, federated learning was implemented to allow decentralized training while preserving sensitive medical data. The results highlight the effectiveness of combining these techniques in the detection of heart disease, providing a scalable and privacy-preserving solution for real-world healthcare applications. Two benchmark datasets were used to validate the proposed approach, ensuring the reliability and generalizability of the findings. Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. This distributed method allows model training without centralizing patient data, so it is compatible with strict data privacy rules. With up to 95% precision, our method shows a notable increase in prediction accuracy according to the testing results. This work offers a strong, scalable, and safe solution for the early identification of cardiovascular diseases by combining ensemble learning, feature selection, and federated learning, opening the way for more general uses in medical diagnostics.
- heart disease detection,
- machine learning,
- federated learning,
- feature selection,
- optimization
Citation: Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui. Federated and ensemble learning framework with optimized feature selection for heart disease detection[J]. AIMS Mathematics, 2025, 10(3): 7290-7318. doi: 10.3934/math.2025334

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Abstract

Predictive models for early identification of heart disease must be precise and efficient because it is a major worldwide health concern. To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. In addition, federated learning was implemented to allow decentralized training while preserving sensitive medical data. The results highlight the effectiveness of combining these techniques in the detection of heart disease, providing a scalable and privacy-preserving solution for real-world healthcare applications. Two benchmark datasets were used to validate the proposed approach, ensuring the reliability and generalizability of the findings. Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. This distributed method allows model training without centralizing patient data, so it is compatible with strict data privacy rules. With up to 95% precision, our method shows a notable increase in prediction accuracy according to the testing results. This work offers a strong, scalable, and safe solution for the early identification of cardiovascular diseases by combining ensemble learning, feature selection, and federated learning, opening the way for more general uses in medical diagnostics.

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