Figure 1.

Training process with grid search cross-validation. Various machine learning models were trained using grid search cross-validation (k-fold: 10). The model demonstrating the highest accuracy in the validation stage was employed to predict the testing data, and the feature importance was investigated. Abbreviations: LR, logistic regression; C, regularization values; kNN, k-nearest neighbor; NB, naïve Bayes; var_smoothing, portion of the largest variance of all features; SVM, support vector machine; RF, random forest; n_trees, number of classifications and regression trees; XGBoost, extreme gradient boosting; n_estimators, number of gradient boosted trees; AHI, apnea–hypopnea index.