Citation

Misbah, Iftu and Shakib, Md. Ibrahim and Islam, Mobinul and Rabbi, Riadul Islam and Othman, Khair Razlan (2025) IoMT-Based Web Application with Machine learning for Real-Time Detection and Monitoring of COVID-19 Patients. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

This study developed a web application for the Internet of Medical Things (IoMT) enhanced with machine learning to detect and monitor COVID-19 patients in real time. We applied machine learning algorithms like Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression to predict COVID-19 based on health indicators such as age, heart rate, temperature, oxygen saturation, and dyspnea, using data from Delta Health Care Chattogram Limited. Our application, built with Streamlit, focuses on easy user interaction and accessibility, proving effective in remote monitoring and early symptom detection in a hospital setting. This approach not only aids in efficient patient management but also helps reduce the healthcare system’s burden, showcasing the potential of IoMT and machine learning in pandemic management and beyond.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	IoMT, machine learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 08:28
Last Modified:	19 Mar 2026 02:48
URII:	http://shdl.mmu.edu.my/id/eprint/15597

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