Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and the sharing of the data is restricted under glo...

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Bibliographic Details
Published in:Applied sciences 2023-02, Vol.13 (3), p.1911
Main Authors: Yaqoob, Muhammad Mateen, Nazir, Muhammad, Khan, Muhammad Amir, Qureshi, Sajida, Al-Rasheed, Amal
Format: Article
Language:English
Subjects:
ABC-SVM
Accuracy
Algorithms
Artificial intelligence
Biomedical data
Cardiovascular disease
Cardiovascular diseases
Classification
Collaboration
Communication
Computer centers
Datasets
Efficiency
Feature selection
Health services
Heart
heart disease prediction
Heart diseases
hybrid technique
intelligence-based healthcare
Internet of Things
Latency
Machine learning
Medical prognosis
Optimization
Predictions
Privacy
privacy-aware machine learning
Support vector machines
Wearable computers
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Summary:One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and the sharing of the data is restricted under global privacy laws. Furthermore, the sharing and collection of biomedical data have a significant network communication cost and lead to delayed heart disease prediction. To address the training latency, communication cost, and single point of failure, we propose a hybrid framework at the client end of HSP consisting of modified artificial bee colony optimization with support vector machine (MABC-SVM) for optimal feature selection and classification of heart disease. For the HSP server, we proposed federated matched averaging to overcome privacy issues in this paper. We tested and evaluated our proposed technique and compared it with the standard federated learning techniques on the combined cardiovascular disease dataset. Our experimental results show that the proposed hybrid technique improves the prediction accuracy by 1.5%, achieves 1.6% lesser classification error, and utilizes 17.7% lesser rounds to reach the maximum accuracy.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13031911