Published on: April 2026
A COMPARATIVE MACHINE LEARNING METHOD FOR HEALTH INDICATOR-BASED SLEEP DISORDER PREDICTION
Thangem Haritha
K Naresh
Article Status
Available Documents
Abstract
Due to shifting lifestyles, elevated stress levels, and inconsistent sleep cycles, sleep disorders have emerged as a major health concern in contemporary culture. In order to avoid major health issues including heart disease, emotional stress, and decreased productivity, early detection of sleep disturbances is crucial. Machine learning methods have demonstrated tremendous promise in recent years for the analysis of medical data and disease prediction. This study suggests a machine learning-based method for classifying sleep problems based on lifestyle and health-related data. The system makes use of a dataset that includes a number of physiological and behavioural characteristics, including age, heart rate, physical activity, stress level, sleep length, and sleep quality.
How to Cite this Paper
Haritha, T. (2026). A Comparative Machine Learning Method for Health Indicator-Based Sleep Disorder Prediction. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.069
Haritha, Thangem. "A Comparative Machine Learning Method for Health Indicator-Based Sleep Disorder Prediction." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.069.
Haritha, Thangem. "A Comparative Machine Learning Method for Health Indicator-Based Sleep Disorder Prediction." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.069.
Search & Index
References
[1] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, USA, 2018.[2] T. Mitchell, Machine Learning, McGraw-Hill Education, New York, USA, 1997.
[3] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, MA, USA, 2016.
[4] S. B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques,” Informatica, vol. 31, no. 3, pp. 249–268, 2007.
[5] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[6] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 06 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.