Integrating Patient History with Explainable Deep AI Using V-shaped and S-shaped Binary Protozoa Optimizer for Depression Detection

In the current era, depression is one of the important psychological well-being situations, necessitating precise categorization for successful treatment. Current detection frameworks for depression underperform due to single-source data use, poor interpretability, and weak generalization across domains. This research introduces the Interpretable Complex Multi-domain Wide Kernel Bayformer (ICMWKB), an innovative methodology that effectively integrates the Complex Multi-Domain Fusion Model (CMDFM) and Knowledge-Sharing Evolutionary Mechanism (KSEM) to strategically combine textual and visual features, thereby enhancing the accuracy of depression detection, signs of depression or not depression, employs a two-level decision strategy and a weight update strategy to balance the convergence. This technique utilizes the Beck Depression Inventory ( BDI) scale for distinguishing between depression and bipolar disorder to enhance the severity level, minimal, mild, moderate, and severe. Furthermore, this incorporates an interpretable framework explained via Local Interpretable Model-agnostic Explanations (LIME), facilitating the empowerment of an interpretation. Additionally, the Domain-Invariant Sequential Per-Frame Feature Extraction (DSPFE) method ensures feature extraction maintains consistency across various recording sources. The V-shaped and S-shaped Binary Artificial Protozoa Optimizer (VSBAPO) enhances the efficiency of text feature selection compared to traditional methods. The suggested model attains an accuracy rate of 98%, accompanied by an AUC of 98.2%, a balanced accuracy of 97.8%, an F1-Score of 98.5%, a precision of 98.5%, an ROC of 98.2%, a specificity of 97.9%, and the quickest processing time of 1.05 s. Overall, the suggested approach involves the detection of depression using a highly complex multi-domain feature set, a more thorough textual selection methodology, and a BDI-based classification system.