Mapping risk of gambling disorder in older adults: a neural network approach

Background and objectives: Gambling disorder (GD) in older adults remains insufficiently understood, despite growing concerns about gambling-related harm in older adulthood. Risk profiles and clinical presentations in this age group may differ from those typically described in younger or mixed-age samples, reflecting age-specific psychosocial contexts, health conditions, and life transitions. Traditional statistical approaches may be limited in their ability to model the complex, non-linear interplay among psychological, behavioral, and contextual factors. The present study aimed to apply neural network-based models to predict the presence and severity of GD in older adults.