Obstructive Sleep Apnea Prediction Using Machine Learning

Abstract

Abstract: A common sleep problem called obstructive sleep apnea (OSA) is characterized by periodic breathing pauses while you're asleep. For OSA to be effectively treated and related health concerns to be avoided, an early and precise diagnosis is essential. Machine learning approaches have become effective tools in recent years for managing and diagnosing OSA. The research that used machine learning techniques for OSA detection and prediction are reviewed in detail in this publication. A large dataset made up of polysomnography recordings and clinical data from OSA sufferers was gathered in order to achieve this. Using this dataset, many machine learning models were trained to categorize the severity levels of OSA or predict the occurrence of OSA, including support vector machines, random forests, and deep neural networks. The findings demonstrated the powerful ability of machine learning algorithms to recognize OSA and distinguish between various severity levels. These models outperformed conventional diagnostic techniques in terms of high sensitivity and specificity rates. To further shed insight on the underlying patterns and processes of the illness, feature selection approaches were used to determine the most pertinent physiological parameters for OSA detection. The effectiveness and precision of screening and therapy strategies may be improved by incorporating machine learning algorithms into OSA diagnosis. This study advances our understanding of the use of machine learning in sleep medicine and paves the door for the creation of automated and individualized OSA diagnostic tools.