Journal of Pharmacology and Pharmacotherapeutics
OnlineFirst
© The Author(s) 2025, Article Reuse Guidelines
https://doi.org/10.1177/0976500X241306184
Altaf O. Mulani1, Kazi Kutubuddin Sayyad Liyakat2, Nilima S. Warade3, Alaknanda Patil4, Mahesh T. Kolte5, Kishor Kinage5, Manish Rana6, Shweta Sadanand Salunkhe7, Vaishali Satish Jadhav8, and Megha Nagrale9
1Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Solapur, Maharashtra, India
2Department of Electronics and Telecommunication, BMIT, Solapur, Maharashtra, India
3Department of Electronics and Telecommunication, AISSMS Institute of Information Technology, Pune, Maharashtra, India
4Department of Electronics and Telecommunication, JSPM NTC, Pune, Maharashtra, India
5Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
6Department of Computer Engineering, St. John College of Engineering & Management (SJCEM), Palghar, Maharashtra, India
7Department of Electronics & Telecommunication, Bharati Vidyapeeth’s College of Engineering for Women, Pune, Maharashtra, India
8Department of Electronics Engineering, Ramrao Adik Institute of Technology, D. Y. Patil University, Navi Mumbai, Maharashtra, India
9Department of Mechanical Engineering, Sardar Patel College of Engineering, Mumbai, Maharashtra, India
Corresponding author(s):
Altaf O. Mulani, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Pandharpur, Solapur, Maharashtra 413304, India. E-mail: draomulani.vlsi@gmail.com
Abstract
Background
Machine Learning-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease.
Purpose
This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection.
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.
Keywords:
Heart disease, wearable sensors, Internet of Medical Things, continuous monitoring, personalized healthcare