Advanced International Journal for Research

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Interpretable Joint Anxiety and Depression Prediction Using Concept Bottleneck Model

Author(s) Rajshri Kashti, Leena Patil
Country India
Abstract Mental health disorders, particularly anxiety and depression, are among the leading causes of globaldisability and frequently co-occur, complicating clinical assessment and treatment planning.Although machine learning techniques have shown promise for mental health screening, manyexisting models operate as opaque black boxes, limiting clinical trust and interpretability. This studypresents an application of Concept Bottleneck Models (CBMs) for joint anxiety-depressionprediction using five psychologically grounded concepts: stress load, lifestyle quality, socialconnectedness, emotional wellbeing, and clinical vulnerability. The proposed CBM reduces featuredimensionality by 95% (21 features → 5 concepts) while achieving RMSE values comparable tobaseline methods (anxiety RMSE of 5.833 versus random forests 5.958, and depression RMSE of5.363 versus 5.315). Five-fold cross-validation yields stable generalization (CV RMSE 5.784 ±0.215), confirming robustness. Coefficient analysis reveals clinically interpretable patterns: SocialConnectedness (β = - 0.107) suppresses anxiety while Clinical Vulnerability (β=0.482) dominatesdepression prediction. Classification F1-macro scores (0.238/0.241) match linear baselineperformance. Our findings demonstrate that CBMs can yield transparent, clinically alignedpredictive models for mental health screening.
Keywords Concept Bottleneck Model, Interpretable Machine Learning, Anxiety-Depression Prediction, Explainable AI, Feature Reduction.
Field Engineering
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-28
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5778
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