AUTHOR=Liu Kang , Geng Shi , Shen Ping , Zhao Lei , Zhou Peng , Liu Wen 

TITLE=Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

JOURNAL=Frontiers in Big Data

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1353469

DOI=10.3389/fdata.2024.1353469

ISSN=2624-909X

ABSTRACT=Objective: To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA)  Methods: We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Results: Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal. Conclusion: Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-tosevere OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasising their significance in clinical assessments. The dataset will be publicly available on my Github.