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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

914
Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
914

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Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.

Kang Liu1, Shi Geng2, Ping Shen1

  • 1Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

Frontiers in Big Data
|May 31, 2024
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Summary

Machine learning models effectively screen for obstructive sleep apnea (OSA), identifying key risk factors like Epworth Sleepiness Scale (ESS) total score and body mass index (BMI) for early intervention.

Keywords:
LightGBMRandom Forestmachine learningobstructive sleep apneaprediction model

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Sleep Medicine Research

Background:

  • Obstructive sleep apnea (OSA) poses significant health risks.
  • Early diagnosis and intervention are crucial for managing OSA.
  • Developing accurate screening tools is essential for clinical practice.

Purpose of the Study:

  • To develop and evaluate machine learning models for obstructive sleep apnea (OSA) screening and diagnosis.
  • To identify key predictors for OSA severity grading and moderate-to-severe OSA screening.
  • To support early clinical detection and management of OSA.

Main Methods:

  • Retrospective analysis of clinical data from 439 patients.
  • Utilized demographic data, medical history, and Epworth Sleepiness Scale (ESS) scores.
  • Compared five machine learning algorithms: XGBoost, LR, SVM, LightGBM, and RF for prediction accuracy.

Main Results:

  • LightGBM demonstrated superior performance in OSA severity grading, highlighting ESS total score, BMI, sex, hypertension, and GERD as key features.
  • Random Forest (RF) excelled in screening moderate-to-severe OSA, with ESS total score, BMI, GERD, age, and dry mouth as primary predictors.
  • ESS total score and BMI were consistently identified as pivotal features across models.

Conclusions:

  • Machine learning models are effective tools for early OSA identification and risk factor analysis.
  • ESS total score and BMI are critical indicators for OSA prediction.
  • Publicly available dataset to facilitate further research and development in OSA diagnostics.