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

ABSTRACT:

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

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SUBMITTER: Qin H 

PROVIDER: S-EPMC10899433 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature