XGBoost model based on HRV discriminated anxiety from comorbid anxiety and depression with AUC 0.8341 and depression from comorbid anxiety and depression with AUC 0.8458 in patients aged ≥14 years.
Retrospective Observational (n=546)
No
Does a machine learning model based on heart rate variability improve the discrimination of anxiety, depression, and their comorbidity in psychiatric patients?
A machine learning model incorporating heart rate variability features can accurately discriminate between anxiety, depression, and their comorbidity, offering a potential objective auxiliary diagnostic tool.
Effect estimate: AUC 0.8341 for anxiety vs comorbidity; AUC 0.8458 for depression vs comorbidity (95% CI 95% CI 0.761-0.907 for anxiety vs comorbidity; 0.775-0.917 for depression vs comorbidity)
Depression and anxiety are significant global public health, their comorbidity share similar clinical symptoms with heterogeneous progression patterns for patients. The early and accurate prediction of anxiety, depression, and comorbidity of anxiety and depression is essential for timely discrimination of high-risk patients, individual treatment strategies, and hierarchical management. This study aimed to develop and validate an interpretable, multi-task prediction model using heart rate variability (HRV) for discriminating anxiety alone, depression alone, and comorbid patients with psychiatric disorders. Patients diagnosed with anxiety or depression by trained psychiatrists were retrospectively enrolled from the Department of Psychiatry, the Affiliated Hospital of Southwest Medical University, between March 2024 and April 2025. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance, and the Recursive feature elimination (RFE) was used for feature selection. Six machine-learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), and naïve Bayes (NB), were adopt to develop multi-task prediction models for discriminating psychiatric comorbidities. Model performances were evaluated by receiver operating characteristic (ROC) curves, accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC). The SHapley Additive exPlanation (SHAP) method was used to visualize the feature contributions and interpret the best model. A total of 546 patients, 114 with anxiety group(20.88%), 100 with depression group (18.32%), and 332 with comorbidity of anxiety and depression (60.81%), were included in this study. The ML-based models were developed for each task using the eleven contributing features. Among the six ML models, the XGBoost achieved the best performance across all tasks. In the validation set, this model achieved the AUCs of 0.8341 (95% confidence interval CI:0.761–0.907) for the task of anxiety versus comorbidity of anxiety and depression, and 0.8458(95% CI:0.775–0.917) for the task of depression versus comorbidity of anxiety and depression, respectively. The clinical utility was evaluated using Decision curve analysis (DCA) and calibration curves. The proposed model has potential clinical application prospects and may assist clinicians to improve the accuracy and efficiency of diagnosis, and thereby facilitating timely appropriate intervention measures for patients with psychiatric disorders. Not applicable.
Shu et al. (Thu,) conducted a retrospective observational in Patients aged ≥14 years with anxiety alone, depression alone, or comorbid anxiety and depression diagnosed by trained psychiatrists (n=546). Machine learning-based multi-task prediction model based on heart rate variability (HRV) vs. Standard diagnosis (clinical interview and scales) or other ML models was evaluated on Discrimination accuracy of ML model to differentiate anxiety vs comorbid anxiety and depression, and depression vs comorbid anxiety and depression using AUC (AUC 0.8341 for anxiety vs comorbidity; AUC 0.8458 for depression vs comorbidity, 95% CI 95% CI 0.761-0.907 for anxiety vs comorbidity; 0.775-0.917 for depression vs comorbidity). XGBoost model based on HRV discriminated anxiety from comorbid anxiety and depression with AUC 0.8341 and depression from comorbid anxiety and depression with AUC 0.8458 in patients aged ≥14 years.