Key result
HRV-based XGBoost machine learning model identifies depression with an AUC of 0.82.
Why the study?
Depression is a major global public health concern requiring timely identification, prompting exploration of the association between heart rate variability and depression to establish machine learning models for auxiliary diagnosis.
Does a machine learning model based on heart rate variability accurately identify depression in outpatients?
Observational (n=465)
No
Does a machine learning model based on heart rate variability accurately identify depression in outpatients?
Effect estimate: AUC 0.82 (95% CI 0.754-0.892)
An interpretable machine learning model (XGBoost) based on heart rate variability can effectively assist in the auxiliary diagnosis of depression.
May aid auxiliary depression diagnosis in outpatients; leaves open prospective validation before clinical adoption.
OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression. METHODS: The data of 465 outpatients from the Affiliated Hospital of Southwest Medical University were selected for the study. The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. The four models were evaluated by the area under the receiver operating characteristic curve (ROC), calibration curve and the decision curve analysis (DCA). Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model. RESULTS: There were 237 people in the depressed group and 228 in the non-depressed group. In the training set (n = 325) and test set (n = 140), the area under of the curve(AUC) values of the XGBoost model are 0.92 [95% confidence interval (CI) 0.888,0.95] and 0.82 (95% CI 0.754,0.892)] respectively, which are higher than the other three models. The XGBoost model has excellent predictive efficacy and clinical utility. The SHAP method was ranked according to the importance of the degree of influence on the model, with age, heart rate, Standard deviation of the NN intervals (SDNN), two nonlinear parameters of HRV and sex considered to be the top 6 predictors. CONCLUSION: We provided a feasibility study of HRV as a potential biomarker for depression. The proposed model based on HRV provides clinicians with a quantitative auxiliary diagnostic tool, which is assist to improving the accuracy and efficiency of depression diagnosis, and can also be utilized for the monitoring and prevention of depression.
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Yang et al. (2024) conducted an observational in Depression (n=465). Heart rate variability (HRV) parameters vs. Non-depressed individuals was evaluated on Area under the receiver operating characteristic curve (AUC) for the XGBoost model in the test set (AUC 0.82, 95% CI 0.754-0.892). An XGBoost machine learning model based on heart rate variability demonstrated excellent predictive efficacy for identifying depression, achieving an AUC of 0.82 in the test set.
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