Sleep Analytics: Machine Learning on IoT-Generated Sleep Health Data

DOI

10.2991/978-94-6239-654-8_23How to use a DOI?

Keywords

Sleep disorders; insomnia; sleep apnea; lifestyle; health analytics; data science; ML algorithms; REPTree; JRIP; Instance based ML algorithm

Abstract

Public Health greatly depends on the sleep disorders such as in Insomnia sleep apnea which is in terms of cardiovascular health, cognitive level functions and quality of the future life. The proposed article offers analysis of sleeping disorders by combining the literature survey with a public data set that includes nearly 374 individual people and 13 different professionals with health-related parameters. Investigation of the Data Analytics used to determine the main relationships between sleep problems and the parameters like stress, BMI, physical activity and cardiovascular indicator. The proposed article emphasizes to support of the health illness by the data driven approaches to detect earlier and build strategies to treating the multifactual nature of sleep problems. The conventional methods of monitoring have some limits in monitoring methods to diagnosis sleeping disorders like Insomnia and sleeping apnea that create serious challenges to the public health. Combination of machine learning algorithms and internet of things Technology integrated allows for a continuous and non-invasive prediction of customers sleep pattern monitoring which is a revolutionary approach to early diagnosis. This article also gives a real time psychological and Lifestyle solutions which can be used to categorize these issues by adding wearable and ambience sensors enabled by the internet with machine learning algorithms. Accuracy and interpretability of several supervised learning models such as decision tree and rule-based classifiers and instant base classifiers where proposed and investigated in this article. During training on data provided by the Internet of Things, machine learning models can detect both sleep apnea and insomnia with a 91% classification accuracy. The explicit diagnostic logic offered by rule-based models improves clinical trust and application. The potential of intelligent sleep analytics systems to enable early diagnosis, tailored therapies, and scalable sleep to aid management in intelligent healthcare environments is highlighted in a research study.

Copyright

© 2026 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  - B. Kalaiselvi
AU  - S. Subbulakshmi
AU  - M. Umamaheswari
AU  - G. K. Agan
PY  - 2026
DA  - 2026/04/24
TI  - Sleep Analytics: Machine Learning on IoT-Generated Sleep Health Data
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 265
EP  - 276
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_23
DO  - 10.2991/978-94-6239-654-8_23
ID  - Kalaiselvi2026
ER  -