JMIR Publications

ABSTRACT

Background:

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), and comorbid insomnia and sleep apnea (COMISA), are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as overnight polysomnography (PSG), the gold standard for diagnosis, is expensive, time-consuming, and cumbersome.

Objective:

We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a PSG test.

Methods:

We applied XGBoost (eXtreme Gradient Boosting) to the data from two medical centers (N=4,257 from Samsung Medical Center and N=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the SHapley Additive exPlanations (SHAP) method. We applied XGBoost using selected features to develop SLEEPS (SimpLe quEstionnairE Predicting Sleep disorders). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve (AUROC).

Results:

Nine features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an AUROC of greater than 0.897 for all three sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the three sleep disorders and shows how the risk changes with changes in weight or age.

Conclusions:

SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.