Sleep Disorder Detection and Classification Using Machine Learning

Sleep disorders are a prevalent health issue, impacting millions of individuals globally through their disruption of quality of life, productivity, and general health. Early and accurate identification of sleep disorders is essential to allow proper treatment and management. This project seeks to develop a machine learning-based system to process sleep data and determine various types of sleep disorders. Through data processing and analysis of pertinent data, the system seeks to provide an auto-generated, effective, and reliable tool to aid in the diagnostic process. The proposed solution seeks to eliminate the constraints of manual diagnosis, such as time constraints and potential human error, by employing computational methods to provide greater precision and consistency. With the objective of aiding improved sleep health care, this project highlights the strength of technology to aid medical diagnosis and enhance our understanding of sleep-related issues.