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Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Rongrong Dai1,2, Kang Yang3,4, Jiajing Zhuang3

  • 1The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.

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Summary
This summary is machine-generated.

Machine learning models using age, gender, BMI, and heart rate can predict obstructive sleep apnea (OSA). The artificial neural network model demonstrated the best performance for early OSA diagnosis and clinical decision-making.

Keywords:
Machine learningMobile sleep medicine management platformObstructive sleep apneaPolysomnographyPrediction model

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Informatics

Background:

  • Obstructive sleep apnea (OSA) is a prevalent condition with identifiable risk factors including age, gender, body mass index (BMI), and mean heart rate during sleep.
  • Accurate prediction of OSA is crucial for timely intervention and management, yet current methods can be complex or inaccessible.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting moderate-to-severe OSA using simple, accessible parameters.
  • To integrate these predictive models into a cloud-based mobile platform for enhanced clinical utility.

Main Methods:

  • Utilized clinical data from 610 patients undergoing polysomnography (PSG).
  • Applied logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms.
  • Trained and validated models using age, gender, BMI, and mean heart rate during sleep as predictors.

Main Results:

  • All six machine learning models effectively predicted moderate-to-severe OSA.
  • The artificial neural network model achieved the highest performance with an AUROC of 80.4% and an accuracy of 69.9%.
  • Other models also showed competitive performance, with AUROCs ranging from 70.4% to 80.2%.

Conclusions:

  • Simple parameters like age, gender, BMI, and heart rate are significant predictors of OSA.
  • Machine learning models, particularly the artificial neural network, offer a reliable tool for early OSA diagnosis.
  • Integration into a mobile platform can improve clinical decision-making for sleep medicine.