Development of machine learning-based predictive models for fertility intentions in patients with Crohn's disease

BackgroundFertility intentions in patients with Crohn's disease (CD) are shaped by complex interactions between biological and psychosocial factors. However, predictive tools that integrate these dimensions remain underdeveloped.ObjectiveThis study aimed to construct and validate interpretable machine learning (ML) models to predict fertility intentions among reproductive-age patients with CD, and to identify key psychosocial determinants driving reproductive decision-making.MethodsA total of 27