Abstract
Obsessive-compulsive disorder (OCD) affects a significant portion of the United States population. The present study investigated the complex relationships among OCD severity, personality traits, religiosity, and spirituality with a dataset of 229 participants. We applied advanced machine and deep learning techniques to identify key predictors of OCD severity, uncovering nuanced relationships and unexpected findings. Notably, item-level features were more influential than aggregate scores, challenging traditional analytical approaches. Moreover, a neural network model, despite not surpassing a linear regression in predictive accuracy, provided a more comprehensive understanding of OCD's heterogeneity and of the nonlinear relationships between our variables. The inclusion of demographic factors provided further explanatory power for predicting OCD severity, emphasizing the multifaceted nature of the disorder. Our results show that machine learning models can nearly match traditional linear models in predictive power while retaining nonlinear relationships essential to understanding OCD. Our study advocates for the adoption of sophisticated predictive models in examining complex psychological phenomena, encouraging a reevaluation of conventional analytical approaches when prediction is central to research questions.