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

Area of Science:

• Pediatric Sleep Medicine
• Artificial Intelligence in Healthcare
• Clinical Diagnostics

Background:

• Obstructive sleep apnea (OSA) diagnosis in children often relies on polysomnography (PSG), a resource-intensive nocturnal sleep study.
• Clinical features obtainable outside of sleep labs are underutilized for OSA screening in pediatric populations.
• Developing non-invasive, accessible diagnostic tools for pediatric OSA is a significant clinical need.

Purpose of the Study:

• To evaluate the efficacy of a machine learning (ML) algorithm for diagnosing pediatric obstructive sleep apnea (OSA).
• To identify key clinical features for OSA diagnosis in children that can be collected in non-medical settings.
• To compare the performance of an ML-based diagnostic model against traditional PSG questionnaire-based models.

Main Methods:

• A cohort of 2464 children (aged 3-18) suspected of OSA underwent clinical data collection and PSG.
• Data were randomly split into training (80%) and testing (20%) sets.
• Elastic Net algorithm was employed for feature selection, followed by model development and validation using stratified 10-fold cross-validation.

Main Results:

• Machine learning models utilizing Elastic Net-selected features achieved an Area Under the Curve (AUC) of 0.73 for AHI ≥5 and 0.78 for AHI ≥10.
• The selected features outperformed those derived from PSG questionnaires.
• Linear Discriminant Analysis demonstrated 44% sensitivity and 90% specificity for OSA identification, indicating clinical utility.

Conclusions:

• A machine learning model leveraging children's clinical features demonstrates effectiveness in diagnosing OSA.
• This approach offers a practical, non-nocturnal alternative for OSA screening and severity stratification in pediatric patients.
• The developed ML model presents a promising tool to improve accessibility and efficiency in pediatric OSA diagnosis.