Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized info...

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Bibliographic Details
Published in:Applied sciences 2022-12, Vol.12 (23), p.12080
Main Authors: Yaqoob, Muhammad Mateen, Nazir, Muhammad, Yousafzai, Abdullah, Khan, Muhammad Amir, Shaikh, Asad Ali, Algarni, Abeer D., Elmannai, Hela
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biomedical data
Cardiovascular disease
Cardiovascular diseases
Data integrity
Diagnosis
Electronic health records
Feature selection
federated learning
Health care
healthcare
Heart
heart disease prediction
Heart diseases
Internet of Things
Machine learning
Medical diagnosis
Optimization
Optimization techniques
Privacy
privacy aware
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Summary:Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122312080