| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02058 | |
| Number of page(s) | 14 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802058 | |
| Published online | 17 November 2023 | |
A Review on Internet of Medical Things (IoMT): A Case Study for Preeclampsia
1 Doctoral Program of Information System, School of Postgraduate Studies Diponegoro University, 50241 Semarang, Central Java, Indonesia
2 Department of Electrical Engineering, Politeknik Negeri Semarang, 50275 Semarang, Central Java, Indonesia
3 Department of Physics, Faculty of Science and Mathematics, Diponegoro University, 50241 Semarang, Central Java, Indonesia
* Corresponding author: sukamto@polines.ac.id
Preeclampsia detection research has started exploring some methods to diagnose and predict preeclampsia. Machine learning (ML) methods and the Internet of Things (IoT) have been successfully implemented in medical research to improve the diagnosis and prevention of complex diseases and syndromes. The goal of this work is to undertake a review of the most recent work on preeclampsia detection. The research focused on articles related to the keywords 'machine learning, 'Internet of Things, 'IoT', 'medical', and preeclampsia in five main databases, namely IEEEXplore, ScienceDirect, SpringerLink, ResearchGate, and ACM Digital Library, etc. We selected and reviewed 90 articles in the end. The final discussion highlights research gaps that remain to be investigated in the cognitive approach to IoT. The study found that preeclampsia detection based on the internet of Medical things (IoMT) was not found, so it became a big opportunity to develop this research in the future.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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