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    Journal of Pharmacology and PharmacotherapeuticsNOW IN
    Article

    the ML-powered Internet of Medical Things (MLIoMT) Structure for Heart Disease Prediction

    Altaf O. Mulani1★, Mohini P. Sardey2, Kishor Kinage3, Shweta Sadanand Salunkhe4, Tanuja Fegade5, Poonam Girish Fegade6★ Corresponding author

    1. 1Department of Electronics & Telecommunication, SKN Sinhgad College of Engineering, Pandharpur, Solapur, Maharashtra, INDIA.
    2. 2Department of Electronics & Telecommunication, AISSMS Institute of Information Technology, Pune, Maharashtra, INDIA.
    3. 3Department of Electronics & Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, INDIA.
    4. 4Department of Electronics & Telecommunication, Bharati Vidyapeeth’s College of Engineering for Women, Pune, Maharashtra, INDIA.
    5. 5Department of Master of Computer Applications, KCES’s Institute of Management and Research, Jalgaon, Maharashtra, INDIA.
    6. 6Department of Master of Computer Applications, K. K. Wagh Institute of Engineering Education and Research, Nashik, Maharashtra, INDIA.

    CORRESPONDENCE

    Altaf O. Mulani

    Department of Electronics & Telecommunication, SKN Sinhgad College of Engineering, Pandharpur, Solapur, Maharashtra, INDIA.

    draomulani.vlsi@gmail.com

    Received: 13-02-2024; Revised: 30-07-2024; Accepted: 05-08-2024.

    Volume 16, Issue 1 · pp. 38–45 · PUBLISHED 2025 · DOI: 10.1177/0976500X241281490

    View on J Pharmacol. Pharmacother. original site ↗

    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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      Mulani, A. O., Sardey, M. P., Kinage, K., Salunkhe, S. S., Fegade, T., & Fegade, P. G. (2025). the ML-powered Internet of Medical Things (MLIoMT) Structure for Heart Disease Prediction. Journal of Pharmacology and Pharmacotherapeutics, 16(1), 38–45. https://doi.org/10.1177/0976500X241281490