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Related Experiment Video

Updated: Jul 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and

Seokmin Ha1,2, Su Jung Choi3, Sujin Lee4

  • 1Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea.

Journal of Medical Internet Research
|September 21, 2023
PubMed
Summary

A new machine learning tool, SLEEPS, accurately predicts the risk of obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia using a simple questionnaire, improving accessibility for diagnosis.

Keywords:
XGBoostcomorbid insomnia and sleep apneainsomniamachine learningobstructive sleep apneapolysomnographyquestionnairesriskrisk predictionsleep

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence in Healthcare
  • Diagnostic Tools

Background:

  • Sleep disorders like obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are prevalent and pose significant health risks.
  • Accurate diagnosis is hindered by underrecognition, time-intensive sleep monitoring, and patient reluctance towards polysomnography.

Purpose of the Study:

  • To develop a machine learning algorithm for predicting OSA, COMISA, and insomnia risk via a simple questionnaire.
  • To eliminate the need for polysomnography in initial sleep disorder risk assessment.

Main Methods:

  • Extreme gradient boosting was applied to data from two medical centers (n=4257 and n=365).
  • Feature selection utilized Shapley additive explanations (SHAP) for optimal predictive power.
  • A questionnaire-based algorithm, SLEEPS, was developed and validated using the area under the receiver operating characteristics curve.

Main Results:

  • Nine key features were identified for the SLEEPS questionnaire.
  • SLEEPS demonstrated high accuracy (AUC > 0.897) for all three sleep disorders across datasets.
  • Distinguishing between COMISA and OSA was crucial for predictive accuracy.

Conclusions:

  • SLEEPS offers a more accessible and convenient approach to sleep disorder risk assessment.
  • A publicly accessible website based on SLEEPS provides a user-friendly tool for risk prediction and management.
  • The tool has the potential to significantly improve sleep disorder diagnosis and treatment accessibility.