Comparative Analysis of Conventional and Ensemble Machine Learning Techniques for Sleep Disorder Classification

Abstract

Computer-aided automatic diagnosis systems have become increasingly important in early detection and management of sleep disorders, as they provide rapid analytical capabilities and assist clinicians in making more accurate and consistent decisions. In this study, two prevalent sleep disorders, apnea and insomnia, were classified using four widely utilized machine learning techniques: Naive Bayes, Support Vector Machine, Logistic Regression, and Random Forest. Each classifier offers a unique analytical perspective, thereby contributing to a more comprehensive assessment of diagnostic performance. To further enhance the robustness and reliability of classification results, three ensemble learning algorithms (AdaBoost, Bagging, and Random Subspace) are employed and integrated with base classifiers. The performance of all models was quantitatively assessed using several key evaluation metrics, including accuracy, kappa coefficient, precision and Area Under the Curve, which together provide a holistic view of classification quality. According to the experimental findings, Logistic Regression stands out as the most effective individual classifier, achieving the highest accuracy rate of 94.667%. Moreover, when Logistic Regression was combined with Bagging or Random Subspace ensemble methods, additional improvements were observed across all evaluation criteria, demonstrating the potential of ensemble-based approaches to further strengthen automated sleep disorder diagnosis.

Keywords

• Classification
• Disease Prediction
• Ensemble Learning
• Machine Learning
• Sleep Disorders

Uyku Bozukluğu Sınıflandırması için Geleneksel ve Topluluk Makine Öğrenmesi Tekniklerinin Karşılaştırmalı Analizi

Abstract

Bilgisayar destekli otomatik tanı sistemleri, hızlı analitik yetenekler sunarak ve klinisyenlerin daha doğru ve tutarlı kararlar almasına yardımcı olarak uyku bozukluklarının erken teşhisinde ve yönetiminde giderek daha önemli hale gelmiştir. Bu çalışmada, yaygın olarak görülen iki uyku bozukluğu olan apne ve uykusuzluk, yaygın olarak kullanılan dört makine öğrenimi tekniği kullanılarak sınıflandırılmıştır: Naive Bayes, Destek Vektör Makinesi, Lojistik Regresyon ve Rastgele Orman. Her sınıflandırıcı, benzersiz bir analitik bakış açısı sunarak tanı performansının daha kapsamlı bir şekilde değerlendirilmesine katkıda bulunur. Sınıflandırma sonuçlarının sağlamlığını ve güvenilirliğini daha da artırmak için, üç topluluk öğrenme algoritması (Uyarlanabilir Yükseltme (AdaBoost), Torbalama (Bagging) ve Rastgele Alt Uzay (Random Subspace)) kullanılmış ve temel sınıflandırıcılarla entegre edilmiştir. Tüm modellerin performansı, doğruluk, kappa katsayısı, kesinlik ve Eğri Altındaki Alan gibi sınıflandırma kalitesinin bütünsel bir görünümünü sağlayan birkaç temel değerlendirme metriği kullanılarak nicel olarak değerlendirilmiştir. Deney sonuçlarına göre, Lojistik Regresyon %94,667 ile en yüksek doğruluk oranını elde ederek en etkili tekil sınıflandırıcı olarak öne çıkmaktadır. Ayrıca, Lojistik Regresyon Torbalama veya Rastgele Alt Uzay topluluk yöntemleriyle birleştirildiğinde, tüm değerlendirme kriterlerinde ek iyileşmeler gözlemlenmiştir. Bu da otomatik uyku bozukluğu tanısını daha da güçlendirmek için topluluk tabanlı yaklaşımların potansiyelini göstermektedir.

Keywords

• Sınıflandırma
• Hastalık Tahmini
• Topluluk Öğrenmesi
• Makine Öğrenmesi
• Uyku Bozuklukları

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