Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

ObjectivesObstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. MethodsWe collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naive Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. ResultsThe RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. ConclusionsThe established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Automated summary

This study aimed to establish machine learning models to screen for the risk of moderate-to-severe and severe obstructive sleep apnea (OSA) based on easily acquired features. Data from 3529 patients in Taiwan were collected, and six common supervised machine learning techniques were utilized. The random forest (RF) produced the highest accuracy (>70%) in screening for both OSA severities.