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Sleep Apnea

Sleep Apnea

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...

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Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques.

Young Jae Kim¹, Ji Soo Jeon¹, Seo-Eun Cho²

¹Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea.

Diagnostics (Basel, Switzerland)

|April 3, 2021

Summary

Machine learning effectively predicts obstructive sleep apnea (OSA) in South Koreans. Support Vector Machine models demonstrated high accuracy, showing promise for clinical OSA diagnosis in the Korean population.

Keywords:

XGBoost logistic regression machine learning obstructive sleep apnea predict model random forest support vector machine

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Sleep Medicine

Background:

Obstructive sleep apnea (OSA) is a prevalent condition requiring accurate diagnostic tools.
Predicting OSA in individuals with suspected cases is crucial for timely intervention.
Current diagnostic methods can be resource-intensive, necessitating efficient alternatives.

Purpose of the Study:

To evaluate the effectiveness of machine learning (ML) algorithms for predicting OSA in a South Korean population.
To identify key clinical variables that contribute to accurate OSA prediction.
To compare the performance of different ML models in diagnosing OSA.

Main Methods:

Collected data from 279 South Koreans with suspected OSA, including 92 clinical variables.
Selected seven major clinical indices for model development.
Trained and validated four ML models: logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB).
Utilized a random split for training and testing datasets.

Main Results:

The Support Vector Machine (SVM) model achieved the highest prediction performance.
SVM demonstrated a sensitivity of 80.33%, specificity of 86.96%, and an Area Under Curve (AUC) of 0.87.
XGBoost showed the lowest performance with an AUC of 0.80.

Conclusions:

Machine learning algorithms exhibit high performance in predicting OSA using clinical data from South Koreans.
The study highlights the potential of ML, particularly SVM, for aiding clinical OSA diagnosis in the Korean population.
ML offers a promising avenue for improving the efficiency and accuracy of OSA prediction in clinical practice.