Ofelia Castro Schepers
Exploring the demographics of teachers who experience secondary traumatic stress
BMC psychology
develop robust machine learning prediction model automatic screening diagnosis obstructive sleep apnea osa using five advanced algorithms namely extreme gradient boosting xgboost logistic regression lr support vector machine svm light gradient boosting machine lightgbm random forest rf provide substantial support early clinical diagnosis intervention conducted retrospective analysis clinical data 439 patients underwent polysomnography affiliated hospital xuzhou medical university october 2019 october 2022 predictor variables demographic information age sex height weight body mass index bmi medical history epworth sleepiness scale ess used univariate analysis used identify variables significant differences dataset divided training validation sets 41 ratio training set established predict osa severity grading validation set used assess model performance using area curve auc additionally separate analysis conducted categorizing normal population one group patients moderatetosevere osa another univariate analysis applied dataset divided training validation sets 41 ratio training set used build prediction model screening moderatetosevere osa validation set used verify models performance among four groups lightgbm model outperformed others top five feature importance rankings ess total score bmi sex hypertension gastroesophageal reflux gerd age ess total score bmi played significant roles dichotomous model rf best performer five models respectively top five ranked feature importance bestperforming rf models ess total score bmi gerd age dry mouth ess total score bmi particularly pivotal machine learningbased prediction models osa disease grading screening prove instrumental early identification patients moderatetosevere osa revealing pertinent risk factors facilitating timely interventions counter pathological changes induced osa notably ess total score bmi emerge critical features predicting osa emphasizing significance clinical assessments dataset publicly available github