Obstructive Sleep Apnea Prediction: A Comprehensive Review and Comparative Study

Abstract

Abstract Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, approximately 110,000 patients with 22,000 having complete entries for all 50 features, to systematically compare the performance of 39 ML/DL models. Our findings highlight the challenging nature of OSA prediction, with accuracies ranging from 29.66% to 46.9% for 4-class prediction and 46.04% to 87.18% for binary tasks. DL models such as DANet and GATE scored highest, whereas ensemble approaches such as LGBM and AdaBoost displayed more consistent performance across folds. However, as severe cases of OSA are easier to predict and over-represented in datasets, accuracy alone is insufficient for model evaluation and we explore a variety of metrics. Finally, imbalance correction and feature selection improved weaker models, but had only marginal effects on the best-performing models. Looking forwards, the development of more sophisticated and tailored DL models and large, high-quality datasets may help to break current performance barriers. We hope that our work can attract more attention to this challenging but interesting research problem.