In-depth Analysis and Classification of Sleep Disorders with Machine Learning Techniques

Year 2024, Volume: 10 Issue: 1, 242 - 260, 30.06.2024

Yunus Emre Gür , Bilal Solak , Mesut Toğaçar

https://doi.org/10.29132/ijpas.1475183

Abstract

This study investigates the link between sleep quality and lifestyle factors in depth. The study analyses the effect of demographic characteristics such as gender, age, and occupation and lifestyle variables such as sleep duration, quality, physical activity levels, and stress on sleep disorders using machine learning techniques. In the study, various machine learning models such as logistic regression, nearest neighbors, naive bayes, random forest, adaBoost classifier, and support vector machine (SVM) were applied. In particular, Random Forest and SVM models were found to be effective in identifying sleep disorders due to their high accuracy rates. In addition, detailed analyses on the relationships between occupation, stress levels, and sleep disorders were performed, and recommendations were presented to improve sleep health.

Keywords

Sleep health, Sleep disorders, Machine learning, Random Forest, Support Vector Machine

Makine Öğrenimi Teknikleriyle Uyku Bozukluklarına Yönelik Derinlemesine Analiz ve Sınıflandırma

Abstract

Bu çalışma, uyku kalitesi ile yaşam tarzı faktörleri arasındaki bağlantıyı derinleme-sine incelemektedir. Araştırma, cinsiyet, yaş ve meslek gibi demografik özellikler ile uyku süresi, kalitesi, fiziksel aktivite düzeyleri ve stres gibi yaşam tarzı değişken-lerinin uyku bozukluklarına etkisini makine öğrenimi teknikleri kullanarak analiz etmektedir. Çalışmada, Lojistik Regresyon, En yakın komşular, Naive Bayes, Rastgele Orman, AdaBoostClassifier ve Destek Vektör Makinesi (SVM) gibi çeşitli makine öğrenimi modelleri uygulanmıştır. Özellikle Rastgele Orman ve SVM mod-elleri, yüksek doğruluk oranları sayesinde uyku bozukluklarını belirlemede etkili oldukları gözlemlenmiştir. Ayrıca, meslek ve stres düzeyleri ile uyku bozuklukları arasındaki ilişkiler üzerine detaylı analizler yapılarak, uyku sağlığının iyileştirilmesi için öneriler sunulmuştur.

Keywords

Uyku sağlığı, Uyku bozuklukları, Makine öğrenimi, Random Forest, Destek Vektör Makinesi

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