Predicting Smartphone Addiction in Teenagers: An Integrative Model Incorporating Machine Learning and Big Five Personality Traits

• Autores: Osorio, Jacobo, Figueroa, Marko, Wong, Lenis
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/673716
• Enlace del recurso: http://hdl.handle.net/10757/673716
• Nivel de acceso: acceso abierto
• Materia: Big Five Personality Traits; Machine Learning; Predictive Model; Random Forest; Smartphone Addiction

Smartphone addiction has emerged as a growing concern in society, particularly among teenagers, due to its potential negative impact on physical, emotional social well-being. The excessive use of smartphones has consistently shown associations with negative outcomes, highlighting a strong dependence on these devices, which often leads to detrimental effects on mental health, including heightened levels of anxiety, distress, stress depression. This psychological burden can further result in the neglect of daily activities as individuals become increasingly engrossed in seeking pleasure through their smartphones. The aim of this study is to develop a predictive model utilizing machine learning techniques to identify smartphone addiction based on the "Big Five Personality Traits (BFPT)". The model was developed by following five out of the six phases of the "Cross Industry Standard Process for Data Mining (CRISP-DM)" methodology, namely "business understanding," "data understanding," "data preparation," "modeling," and "evaluation." To construct the database, data was collected from a school using the Big Five Inventory (BFI) and the Smartphone Addiction Scale (SAS) questionnaires. Subsequently, four algorithms (DT, RF, XGB LG) were employed the correlation between the personality traits and addiction was examined. The analysis revealed a relationship between the traits of neuroticism and conscientiousness with smartphone addiction. The results demonstrated that the RF algorithm achieved an accuracy of 89.7%, a precision of 87.3% the highest AUC value on the ROC curve. These findings highlight the effectiveness of the proposed model in accurately predicting smartphone addiction among adolescents.