Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis - Qin, Han; Zhang, Liping; Li, Xiaodan; Xu, Zhifei; Zhang, Jie; Wang, Shengcai; Zheng, Li; Ji, Tingting; Mei, Lin; Kong, Yaru; Jia, Xinbei; Lei, Yi; et al. (Frontiers in Pediatrics, 2024)
Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments Patients and Methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.Results: Feature selection using Elastic Net resulted in 47 features for AHI≥5 and 31 features for AHI≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI≥5 and 0.78 for AHI≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.: This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.