Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study

Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study

<b>Background:</b> Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches...

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Bibliographic Details
Main Authors: Mohammed A. Mamun, Jannatul Mawa Misti, Md Emran Hasan, Firoj Al-Mamun, Moneerah Mohammad ALmerab, Johurul Islam, Mohammad Muhit, David Gozal
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Brain Sciences
Subjects:
daytime sleepiness
machine learning
predictive modeling
CatBoost
feature importance
adolescents
Online Access:https://www.mdpi.com/2076-3425/14/10/1015

Internet

https://www.mdpi.com/2076-3425/14/10/1015

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