Explainable artificial intelligence systems for predicting mental health problems in autistics

The recognition of mental disorder symptoms is crucial for timely management and reduction of recurring symptoms and disabilities. The ability to predict and explain mental health challenges can enable earlier intervention and more effective, individualized care plans, improving the overall well-being of people with autism. Consequently, artificial intelligence (AI) methods have been applied to assist psychologists and psychiatrists in decision-making processes by analyzing patients’ medical histories and behavioral data. The current models for diagnosing mental health disorders (MHD) suffer from a lack of interpretability. This study introduces the E xplainable M ental H ealth D isorders (EMHD) model, a robust framework that leverages machine learning algorithms and Explainable Artificial Intelligence (XAI) to identify mental health disorders in young children, including toddlers. The EMHD consists of two main components: an ensemble model and Explainable Artificial Intelligence (XAI). First, an ensemble model known as Voting, which uses different feature selection techniques, namely Mutual Information (Mutinfo), Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE), is applied to classify the MHD dataset. Second, XAI is integrated into the proposed framework to provide transparency and explanations for the model’s decision-making process. To achieve that, the model are explained using a well-known XAI technique called Shapley Additive Explanations (SHAP). The proposed EMHD demonstrates superior performance across all evaluation metrics, achieving an accuracy, precision, recall, and F1-Score of 1.0, in comparison to other baseline models. Furthermore, the study highlights the potential of XAI to provide personalized and actionable insights to mental health professionals who work with autistic individuals. Finally, this study can address the pressing MHD crisis in Saudi Arabia and significantly improve early MHD diagnosis.