Citation

Pang, Wai Leong and Phang, Swee King and Idrus, Intan Izafina and Chan, Kah Yoong and Prasetio, Murman Dwi and Ting, Joseph (2024) Machine Learning Based Smart Health Monitoring System for COVID-19. In: Intelligent Systems Modeling and Simulation III. Springer, pp. 71-95.

Full text not available from this repository.

Abstract

The COVID-19 virus, highly contagious and prone to rapid spread without proper measures, necessitates an urgently needed smart monitoring system. This system aims to monitor COVID-19 symptoms, alleviating the burden on the healthcare system. A proposed Health Monitoring System meticulously tracks various characteristics closely linked to COVID-19 symptoms and is categorized into four parts: Sensor, Data Processing, Connectivity & Cloud, and User Interface & Actuator. The design and selection of components undergo thorough comparison, and the total cost of the developed prototype is a mere USD 30. Four Machine Learning Algorithms are evaluated through extensive experimental tests according to their F1-score, Accuracy, Precision, and Recall. The Decision Tree Classifier emerges with the highest accuracy at 96.72%, accompanied by relatively high F1-score, precision, and recall. The prototype is designed to collect data on the most recent COVID-19 symptoms, utilizing machine learning for predictive analysis. Multiple functionalities, including data logging, IoT connectivity with data visualization, and an Email notification system, enhance its utility. Importantly, this prototype remains applicable even in the post-pandemic era

Item Type:	Book Section
Uncontrolled Keywords:	Machine Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2024 01:46
Last Modified:	04 Nov 2024 01:46
URII:	http://shdl.mmu.edu.my/id/eprint/13112

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