#GAMEADDICTED: A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention

This study addresses the growing concern of digital game addiction (DGA) by developing an information system for detection and early intervention, to predict an individual’s propensity toward DGA. The study employed machine learning models to process unstructured data collected from online ga...

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Auteurs principaux:	Esra Kahya Ozyirmidokuz, Bekir Asim Celik, Eduard Alexandru Stoica
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2025-01-01
Collection:	IEEE Access
Sujets:	Digital game addiction data analytics early diagnosis machine learning text classification natural language processing
Accès en ligne:	https://ieeexplore.ieee.org/document/11075684/
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