Explainable AI models for identifying anxiety and distress in cardiac patients with ICDs

BACKGROUND: Cardiac patients with Implantable Cardioverter Defibrillators (ICDs) are at elevated risk of experiencing anxiety, depression, or general psychological distress. Nevertheless, routine psychological screening in cardiology care is limited, and early identification of vulnerable individuals remains a challenge, particularly in settings transitioning to remote monitoring. Scalable, data-driven tools are urgently needed to support timely detection and intervention. METHODS: We developed Machine Learning (ML) models to predict anxiety and psychological distress using patient-reported outcomes from the ACQUIRE-ICD dataset (N = 478). The methodology included imputing values, selecting features strategically, and tuning hyperparameters. Nine ML algorithms and two ensemble approaches (stacking and voting) were evaluated. The best-performing models were interpreted using SHAP (SHapley Additive exPlanations) to enhance clinical transparency. RESULTS: The stacking classifier achieved an F1-macro score of 0.93 for anxiety detection, while the light gradient boosting machine reached 0.93 for distress prediction. Feature importance analyses revealed that Mental Health Scores (MCS), loneliness scores, depression and anxiety scales, and quality-of-life indices were the most predictive features. Balancing methods such as SMOTE-ENN did not improve model performance and were excluded from final configurations. CONCLUSION: Our findings demonstrate that combining clinical insight, structured preprocessing, and ensemble learning leads to highly accurate and explainable models for detecting psychological distress in ICDs patients. The integration of SHAP enables interpretability and supports clinical trust, paving the way for scalable decision-support tools in cardiology. Future work should explore external validation and real-world deployment. CLINICAL TRIAL: Not applicable