International Journal of Computer Techniques Volume 12 Issue 5 | Advancing Sleep Disorder Diagnosis Through Machine Learning Algorithms
Advancing Sleep Disorder Diagnosis Through Machine Learning Algorithms
Author: G. Mounika
MCA Student, Department of Information Technology, Jawaharlal Nehru Technological University, India
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 5 | Page: 60 | Publication Date: September – October 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper proposes a machine learning–based system for automatic classification of sleep disorders. Using curated datasets and models like Logistic Regression, Random Forest, Gradient Boosting, and KNN, the system improves diagnostic accuracy and scalability. A Django interface supports user interaction and visualization. The approach reduces manual effort and enhances healthcare efficiency.
Keywords
Sleep-stage classification, Machine Learning, Automated Diagnosis, Sleep Quality
Conclusion
The system demonstrates improved classification accuracy and robustness over manual methods. It supports scalable healthcare applications and lays the groundwork for integration with real-time monitoring systems. Future work may explore multimodal data fusion and deployment in clinical settings.
References
Includes references from COLING, ACL, SIGKDD, IEEE, and Pattern Recognition covering product classification, multimodal fusion, and hierarchical learning in sleep and e-commerce domains.
