Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis
Han Qin,
Liping Zhang,
Xiaodan Li
et al.
Abstract:ObjectiveThe 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 methodsThis 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 rando… Show more
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