Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

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...

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Bibliographic Details
Main Authors: Han Qin, Liping Zhang, Xiaodan Li, Zhifei Xu, Jie Zhang, Shengcai Wang, Li Zheng, Tingting Ji, Lin Mei, Yaru Kong, Xinbei Jia, Yi Lei, Yuwei Qi, Jie Ji, Xin Ni, Qing Wang, Jun Tai
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Pediatrics
Subjects:
obstructive sleep apnea
machine learning
artificial intelligence
computer-aided diagnosis
children
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2024.1328209/full

Internet

https://www.frontiersin.org/articles/10.3389/fped.2024.1328209/full

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