Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students

Figure 4 Feature importance analysis based on SHapley Additive exPlanations method. The heart rate variability parameters identified by SHapley Additive exPlanations (SHAP) for the random forest model are ranked according to their importance, from most to least. A: Mean absolute SHAP values (bar plot). Feature importance is assessed by computing the mean of the absolute SHAP values for each feature. A bar plot illustrates the mean absolute SHAP values for the heart rate variability parameters, with larger bars indicating greater importance in distinguishing between stress and non-stress states; B: SHAP value distribution (beeswarm plot). Each point represents the SHAP value for an individual sample, red and blue colors indicating higher and lower values, respectively. DPTI/SPTI: Diastolic/Systolic Pressure-Time Index of the Heart; TDI: Time Domain Index; FDI: Frequency Domain Index; SDNN: Standard Deviation of NN Intervals; VLF: Very Low Frequency; SDNN5: 5-Minute Mean of Standard Deviation of NN Intervals; C1: Compliance of arterial vascular volume; AI: Augmentation Index; TSP: Total Power Spectrum; TP: Total Power; EEI: Ejection Elasticity Index; SHAP: SHapley Additive exPlanations.