Health Recommender System for Sleep Apnea using Computational Intelligence

Abstract

In health care, there is a growing interest on building recommendation systems for sleep apnea management. These systems use data from a variety of sources, including patient-reported outcomes and electronic health records, to assess sleep quality, breathing patterns, and medical treatment adherence. Leveraging artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and cloud platforms, the system analyzes these data to uncover patterns and correlations. It then creates individualized patient profiles that incorporate details about diet, medical history, and sleep habits. Based on these profiles, customized recommendations are generated to enhance sleep apnea management. These recommendations may encompass treatment options and lifestyle adjustments, Yoga, exercise, etc. to improve treatment effectiveness and overall well-being for individuals with sleep apnea. This review article discusses available literature on sleep apnea, its diagnosis, and the role played by ML and deep learning classifiers in the prediction and classification of the disease. The article also presents a comparative analysis on performance measures for these methods. This article highlights the research scope for incorporating technologies such as AI, the IoT, and computational intelligence in improving the diagnosis, remote monitoring, and treatment of sleep apnea.