Comprehensive Evaluation of Machine Learning Techniques for Obstructive Sleep Apnea Detection

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dc.contributor.authorDJELLAL Adel (Co-Auteur)
dc.date.accessioned2025-09-11T08:09:56Z
dc.date.available2025-09-11T08:09:56Z
dc.date.issued2024
dc.description(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 15, No. 12, 2024. www.ijacsa.thesai.org
dc.description.abstractObstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results
dc.identifier.urihttp://dspace.ensti-annaba.dz:4000/handle/123456789/762
dc.language.isoen
dc.publisherInternational Journal of Advanced Computer Science and Applications
dc.subjectobstructive sleep apnea
dc.subjectrandom
dc.subjectforest classifier
dc.subjectoversampling
dc.titleComprehensive Evaluation of Machine Learning Techniques for Obstructive Sleep Apnea Detection
dc.typeArticle
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