Advanced Machine Learning Techniques for High-Precision Health Detection from Sweat Biomarkers

DOI

10.2991/978-94-6463-754-0_21How to use a DOI?

Keywords

Machine Learning Techniques; PPG sensor; IR; Health conditions

Abstract

This project implements machine learning (ML) algorithms to detect and predict the type of diseases that occur, based on the continuous data collected from the various sensors, which helps in real-time health monitoring. The ML components are used to improve the system capabilities in the medical sector. The data from multiple sensors like IR, PPG, and pH sensors are given to the ML model, which will be trained to identify the patterns and indications of different health conditions. The ML model will holds older health data, including patterns associated with glucose, hemoglobin, and sodium levels, along with additional similar parameters. By using supervised learning, the model is trained to recognize the difference between normal people and diseased people and predict the type of health issue that occurs to that person. By uninterrupted analyzing and learning data from the various sensor, the ML component will be able to give personalized risk assessments and make prediction based on analyses. This Differences in glucose levels over a specific period of time can indicate risk of diabetes, while a trend in hemoglobin levels can indicate anemia. The use of ML-based disease prediction incorporates the realm of preventive health care, empowering individuals to take tailored actions and medical interventions in time must These combinations of features such as raw modeling and machine learning for disease prediction not only deliver real-time information about current healthcare Contributions to individuals on a comprehensive health care system.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Radhika
AU  - K. Guruprakash
AU  - R. Gauthama Ragul
AU  - C. M. Deenanath
PY  - 2025
DA  - 2025/06/30
TI  - Advanced Machine Learning Techniques for High-Precision Health Detection from Sweat Biomarkers
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 224
EP  - 237
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_21
DO  - 10.2991/978-94-6463-754-0_21
ID  - Radhika2025
ER  -