Background: Gambling disorders are an escalating public health issue, with notable increases across age groups, particularly among adolescents and young adults. This study examines the role of coping strategies in gambling behaviors among university students aged 17–48 years and explores the prediction potential of artificial neural networks. Methods: The sample included 218 participants (M = 21.89, SD = 5.57). Results: A multilayer perceptron neural network was implemented to classify gambling risk based on coping strategies. Significant correlations between specific coping strategies and higher levels of gambling disorders were revealed. The neural network model demonstrated an 85% accuracy in predicting gambling risk, with the most influential factors identified as autonomy, negative urgency, gender, denial, and lack of perseverance. Conclusions: These findings highlight the effectiveness of neural networks in identifying individuals most at risk for GDs.
Giménez-Lozano et al. (Thu,) studied this question.