Multidimensional Cognitive and Linguistic Profiles in Adolescent Schizo-Obsessive Presentations: A Machine Learning and Explainable Artificial Intelligence Approach

Schizo-obsessive presentations in adolescence remain insufficiently characterized, particularly with respect to their cognitive, linguistic, and social-cognitive features. This study explored clinical, neurocognitive, social-cognitive, and thought–language characteristics associated with schizophrenia, obsessive-compulsive disorder (OCD), and schizo-obsessive presentations in adolescents using a Support Vector Machine–based explainable machine learning framework. A total of 182 adolescents were included: schizophrenia with comorbid OCD ( n = 27), schizophrenia without obsessive-compulsive symptoms ( n = 55), and OCD without psychotic symptoms ( n = 100). The model demonstrated high discriminative performance overall, although classification performance was lower for the schizo-obsessive group, suggesting partial overlap with both schizophrenia and OCD presentations. Shapley Additive exPlanations (SHAP) analyses indicated that obsessive-compulsive symptom burden and negative symptom burden were the primary dimensions associated with classification, while social cognition, verbal fluency, verbal memory, executive functioning, processing speed, thought–language organization, and prosodic features also contributed to group differentiation. The findings suggest that schizo-obsessive presentations may involve broader cognitive and linguistic characteristics beyond symptom-level overlap and support a multidimensional approach to their clinical evaluation.