Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO
Abstract Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, and nerve ailments. Early predict...
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| Format: | Article |
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Springer
2025-08-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00956-8 |
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| author | Sachikanta Dash Sasmita Padhy Preetam Suman Sandip Mal Lokesh Malviya Amrit Suman Jaydeep Kishore |
| author_facet | Sachikanta Dash Sasmita Padhy Preetam Suman Sandip Mal Lokesh Malviya Amrit Suman Jaydeep Kishore |
| author_sort | Sachikanta Dash |
| collection | DOAJ |
| description | Abstract Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, and nerve ailments. Early prediction mitigates risks. All these encompassing strategies are designed to improve prediction precision and facilitate proactive diabetes control. This research employed SMOTE methods to tackle imbalanced classes, utilizing various classification algorithms such as Random Forest, XGBoost, Multilayer Perceptron, Gradient Boost, and AdaBoost. Following extensive training and evaluation, the AdaBoost classifier delivered superior outcomes, achieving a 94.02% accuracy rate, an F1 score of 93.32%, and an AUC of 0.95. In the healthcare industry, accurately forecasting diabetes mellitus is crucial; however, privacy laws hinder the transfer of medical information from the Internet of Medical Things (IoMT), causing delays in diagnosis. This study introduces the Federated Learning with Weighted Conglomeration Optimization (FLWCO) model as a solution to these challenges. In Centralized Learning, AdaBoost with WCO achieves an accuracy of 95.32% when tested on a Kaggle dataset consisting of 96,146 instances. During the second stage, FLWCO achieves a superior 97.27% accuracy rate compared to other federated learning techniques. The method not only guarantees privacy conformity but also decreases communication expenses. FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. Furthermore, the proposed model can be employed to estimate the likelihood of heart disease in individuals with diabetes. This highlights the potential of federated learning, especially FLWCO, in leveraging distributed data while preserving privacy, facilitating accurate diabetes mellitus diagnosis, and addressing challenges in sharing medical information securely and efficiently. |
| format | Article |
| id | doaj-art-b053fabe36534e48ab4d035fca833bab |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-b053fabe36534e48ab4d035fca833bab2025-08-24T11:49:39ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-08-0118112610.1007/s44196-025-00956-8Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCOSachikanta Dash0Sasmita Padhy1Preetam Suman2Sandip Mal3Lokesh Malviya4Amrit Suman5Jaydeep Kishore6Department of CSE, GIET UniversitySchool of Computing Science and Engineering, VIT Bhopal UniversitySchool of Computing Science and Engineering, VIT Bhopal UniversitySchool of Computing Science and Engineering, VIT Bhopal UniversitySchool of Computing Science and Engineering, VIT Bhopal UniversityDepartment of Computer Science and Engineering, School of Engineering and Technology, Sharda UniversityDepartment of AIML, Manipal University JaipurAbstract Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, and nerve ailments. Early prediction mitigates risks. All these encompassing strategies are designed to improve prediction precision and facilitate proactive diabetes control. This research employed SMOTE methods to tackle imbalanced classes, utilizing various classification algorithms such as Random Forest, XGBoost, Multilayer Perceptron, Gradient Boost, and AdaBoost. Following extensive training and evaluation, the AdaBoost classifier delivered superior outcomes, achieving a 94.02% accuracy rate, an F1 score of 93.32%, and an AUC of 0.95. In the healthcare industry, accurately forecasting diabetes mellitus is crucial; however, privacy laws hinder the transfer of medical information from the Internet of Medical Things (IoMT), causing delays in diagnosis. This study introduces the Federated Learning with Weighted Conglomeration Optimization (FLWCO) model as a solution to these challenges. In Centralized Learning, AdaBoost with WCO achieves an accuracy of 95.32% when tested on a Kaggle dataset consisting of 96,146 instances. During the second stage, FLWCO achieves a superior 97.27% accuracy rate compared to other federated learning techniques. The method not only guarantees privacy conformity but also decreases communication expenses. FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. Furthermore, the proposed model can be employed to estimate the likelihood of heart disease in individuals with diabetes. This highlights the potential of federated learning, especially FLWCO, in leveraging distributed data while preserving privacy, facilitating accurate diabetes mellitus diagnosis, and addressing challenges in sharing medical information securely and efficiently.https://doi.org/10.1007/s44196-025-00956-8Diabetes classificationFederated learningCardiovascular diseaseWeighted conglomeration optimization |
| spellingShingle | Sachikanta Dash Sasmita Padhy Preetam Suman Sandip Mal Lokesh Malviya Amrit Suman Jaydeep Kishore Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO International Journal of Computational Intelligence Systems Diabetes classification Federated learning Cardiovascular disease Weighted conglomeration optimization |
| title | Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO |
| title_full | Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO |
| title_fullStr | Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO |
| title_full_unstemmed | Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO |
| title_short | Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO |
| title_sort | privacy preserving diabetes and heart disease prediction via federated learning and wco |
| topic | Diabetes classification Federated learning Cardiovascular disease Weighted conglomeration optimization |
| url | https://doi.org/10.1007/s44196-025-00956-8 |
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