Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

The abundant existence of both structured and unstructured data rapid advancement statistical models stressed the importance introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified classified at early stage itself order reduce health crisis. With this background, current paper presents XAI-based ASD diagnosis (XAI-ASD) model detect classify precisely. proposed XAI-ASD technique involves design Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Algorithm (WOA) with Deep Belief Network (DBN) also applied for classification which hyperparameters DBN are optimally tuned help WOA. ensure better diagnostic outcome, series simulation was conducted on dataset.