ML-powered Internet of Medical Things (MLIoMT) Structure for Heart Disease Prediction
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Date
2025-03
Journal Title
Journal ISSN
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Publisher
Sage Publications India Pvt. Ltd.
Abstract
Background: ML-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease. Objectives: This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection. Materials and Methods: Through the integration of wearable sensors, mobile applications, cloud computing, and advanced ML techniques, MLIoMT enables continuous monitoring of vital signs and cardiac health indicators in real time. By analyzing this data stream, abnormalities indicative of heart disease can be detected early, facilitating timely intervention and personalized healthcare recommendations. The MLIoMT framework employs diverse ML methods, such as deep learning and ensemble techniques to enhance the accuracy and reliability of heart disease prediction models. Results: The proposed structure holds promise for revolutionizing preventive healthcare, enabling proactive management of cardiac health, and ultimately reducing the burden of heart disease. Results in terms of accuracy, precision, recall and F1 score show that the proposed system has better performance and efficiency. Conclusion: Overall, MLIoMT represents a significant advancement in healthcare technology, with the potential to improve patient outcomes and enhance overall quality of life.
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Keywords
Heart disease, wearable sensors, internet of medical things, continuous monitoring, personalized healthcare
Citation
Mulani AO, Sardey MP, Kinage K, Salunkhe SS, Fegade T, Fegade PG.. ML-powered Internet of Medical Things (MLIoMT) Structure for Heart Disease Prediction. Journal of Pharmacology and Pharmacotherapeutics. 2025 Mar; 16(1): 38-45