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<p>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 <b>(</b>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月28日

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  • Pratiksha Meshram,
  • Radha Krishna Rambola

摘要

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.