CARE: Comprehensive Artificial Intelligence Techniques for Reliable Autism Evaluation in Pediatric Care

Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition, we applied Shapley additive explanations (SHAP), an explainable artificial intelligence (XAI) technique, to improve the interpretability of the models. Models developed using CatBoost and other AI algorithms showed high accuracy in diagnosing ASD in children. SHAP provided clear insights into the influence of each variable on diagnostic outcomes, making model decisions transparent and understandable to healthcare professionals. By integrating robust machine learning methods with user-friendly tools such as PyCaret and leveraging XAI techniques such as SHAP, this study contributes to the development of reliable, interpretable, and accessible diagnostic tools for ASD. These advances hold great promise for supporting informed decision-making in clinical settings, ultimately improving early identification and intervention strategies for ASD in the pediatric population. However, the study is limited by the dataset’s demographic imbalance and the lack of external clinical validation, which should be addressed in future research.