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Fig. 4 | BMC Anesthesiology

Fig. 4

From: Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study

Fig. 4

SHAP Value Analysis for XGBoost Model. This figure provides a comprehensive analysis of feature importance using SHAP values for the optimized XGBoost model predicting postoperative pain outcomes following cesarean sections. A: This panel illustrates how various predictors influence the model’s output. Esketamine use is identified as the most influential factor, significantly delaying the first activation of PCIA. Anesthesia methods and anesthetic drug types also demonstrate substantial impacts on the timing of PCIA activation. B: This bar chart quantifies the average absolute SHAP values, summarizing the impact of each feature on the model's performance. The visualization confirms the dominant influences of esketamine use, anesthesia methods, and anesthetic drug types in determining the model's predictions

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BMC Anesthesiology

ISSN: 1471-2253

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