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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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Obstructive Sleep Apnea: A Prediction Model Using Supervised Machine Learning Method.

Zahra Keshavarz¹, Rita Rezaee², Mahdi Nasiri²

¹Student research committee, School of Management and Medical information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.

Studies in Health Technology and Informatics

|July 2, 2020

Summary

This summary is machine-generated.

Supervised machine learning effectively predicts Obstructive Sleep Apnea (OSA) using non-invasive features. Naïve Bayes and Logistic Regression models show promise for early OSA screening by physicians.

Keywords:

Data Mining Obstructive Sleep Apnea Prediction Supervised Machine Learning Methods

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Published on: December 6, 2016

Area of Science:

Medical Informatics
Machine Learning
Sleep Medicine

Background:

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder linked to significant health risks.
Early detection and management of OSA are crucial for mitigating associated complications.

Purpose of the Study:

To evaluate supervised machine learning algorithms for predicting Obstructive Sleep Apnea (OSA).
To assess the efficacy of models using non-invasive patient features.

Main Methods:

Utilized the CRISP-DM methodology for data preprocessing, including imputation of missing values.
Applied and compared popular machine learning algorithms (e.g., Naïve Bayes, Logistic Regression, SVM) on a dataset of 231 records.
Employed 10-fold cross-validation for robust performance evaluation.

Main Results:

Naïve Bayes (AUC 0.768) and Logistic Regression (AUC 0.761) demonstrated the highest predictive performance.
Support Vector Machine (SVM) achieved 93.42% sensitivity, while Naïve Bayes offered 59.49% specificity.
The models, using readily available features, showed adequate discriminatory power for OSA prediction.

Conclusions:

Supervised machine learning models can effectively predict Obstructive Sleep Apnea using non-invasive features.
These models can serve as a valuable supplementary tool for physicians in screening high-risk individuals for OSA.