KMAN Publication Inc. (KMANPUB) Journal of Assessment and Research in Applied Counseling (JARAC) 8 Serial Number 29 2026 04 01 Modeling Generalized Anxiety Disorder from Intolerance of Uncertainty and Attentional Control Using AI‑Driven Feature Selection Modeling Generalized Anxiety Disorder from Intolerance of Uncertainty and Attentional Control Using AI‑Driven Feature Selection 1 12 10.61838/kman.jarac.5210 EN Journal Article 2026 01 13 <p><strong>Objective: </strong>The primary objective of this study was to identify the most robust, item-level cognitive predictors of generalized anxiety disorder severity utilizing an artificial intelligence-driven hybrid feature selection pipeline applied to the constructs of intolerance of uncertainty and attentional control.</p> <p><strong>Methods and Materials: </strong>A cross-sectional predictive study design was utilized with a sample of 486 Canadian adults. Participants completed standardized self-report measures, including the 7-item Generalized Anxiety Disorder Assessment (GAD-7), the 12-item Intolerance of Uncertainty Scale (IUS-12), and the 20-item Attentional Control Scale (ACS). Data preprocessing involved k-nearest neighbors imputation for missing values and standardization of continuous variables (z=(x-μ)/σ). A hybrid machine learning approach combining Least Absolute Shrinkage and Selection Operator (LASSO) regression and Extreme Gradient Boosting Recursive Feature Elimination (XGBoost-RFE) was employed to extract key item-level predictors. The dataset was split into an 80%training set and a 20%testing set, with model hyperparameter tuning validated via 10-fold cross-validation.</p> <p><strong>Findings: </strong>The sample (N=486; 61.3%female; age M=34.2, SD=10.5) exhibited significant baseline correlations between GAD-7 and both IUS-12 (r=.64,p&lt;.001) and ACS (r=-.52,p&lt;.001). From an initial pool of 32cognitive items, the hybrid AI pipeline (using an optimal LASSO penalty of α=0.034) identified a critical subset of just 7key features (4 capturing distress/paralysis from uncertainty and 3capturing difficulties in attentional shifting/focusing). The optimized XGBoost model utilizing these 7selected features achieved superior predictive performance on the test set (R^2=0.73, RMSE=2.40) compared to the baseline model utilizing all 32features (R^2=0.61, RMSE=2.88).</p> <p><strong>Conclusion: </strong>Granular, item-level cognitive vulnerabilities—specifically uncertainty-induced paralysis and severe deficits in attentional shifting—are the primary drivers of anxiety severity, demonstrating the transformative potential of AI in precise psychiatric modeling.</p> https://journals.kmanpub.com/index.php/jarac/article/download/5210/9443