Early Detection of Sleep Disorders Using Ensemble Boosting and Classical ML with Lifestyle Data

Sleep disorders are increasingly prevalent in modern society, significantly impacting quality of life, productivity, and physical and mental health. This study distinguishes itself by evaluating six machine learning algorithms—Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor (KNN), XGBo...

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Auteurs principaux:	Sendi Novianto, Muhammad Rayhan Ramadhani
Format:	Artigo
Langue:	Inglês
Publié:	Politeknik Negeri Batam 2026-04-01
Collection:	Journal of Applied Informatics and Computing
Sujets:	sleep disorder machine learning ensemble boosting classical machine learning predictive modelling lifestyle data
Accès en ligne:	https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/12317
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