Machine learning-based preoperative datamining can predict the therapeutic outcome of sleep surgery in OSA subjects

Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep apnea (OSA) and the prediction of whether sleep surgery will have successful outcome is very important. The aim of this study is to assess a machine learning-based clinical model that predict the success rate of sleep surgery in OSA subjects. The predicted success rate from machine learning and the predicted subjective surgical outcome from the physician were compared with the actual success rate in 163 male dominated-OSA subjects. Predicted success rate of sleep surgery from machine learning models based on sleep parameters and endoscopic findings of upper airway demonstrated higher accuracy than subjective predicted value of sleep surgeon. The gradient boosting model showed the best performance to predict the surgical success that is evaluated by pre- and post-operative polysomnography or home sleep apnea testing among the logistic regression and three machine learning models, and the accuracy of gradient boosting model (0.708) was significantly higher than logistic regression model (0.542). Our data demonstrate that the data mining-driven prediction such as gradient boosting exhibited higher accuracy for prediction of surgical outcome and we can provide accurate information on surgical outcomes before surgery to OSA subjects using machine learning models.