Data Based Framework for Sleep Medicine

Obstructive Sleep Apnea (OSA) is a common breathing disorder that affects between 2 % and 26 % of adults and remains significantly underdiagnosed, leading to disturbed sleep and reduced oxygen levels due to repeated collapse of the upper airway during sleep. In order to effectively diagnose and treat OSA, it is essential to use a machine learning (ML) model to predict future trends based on past data. This paper explores the integration of machine learning techniques into the field of sleep medicine, specifically for the diagnosis and management of OSA. The proposed digital platform offers a promising solution that improves diagnostic accuracy, supports personalized medicine, and improves the efficiency of healthcare delivery.