Fig. 7 | Scientific Reports

Fig. 7

From: Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer

Fig. 7

SHAP waterfall plots showing individual patient predictions with feature contributions. A-D, Four representative cases demonstrating how different clinical and laboratory features contribute to lymph node metastasis (LNM) prediction. Each horizontal bar represents a feature’s contribution to the final prediction, with red bars indicating positive contributions (increasing lymph node metastasis risk) and blue bars indicating negative contributions (decreasing risk). The x-axis shows the prediction value, starting from the expected value (E[f(x)]) and ending at the final prediction f(x). Feature values are displayed next to each bar. A, Patient with T1 stage shows f(x) = − 1.36 and indicates low-risk prediction. B, Patient with T2 stage shows f(x) = − 0.428 and indicates low-risk prediction. C, Patient with T3 stage shows f(x) = 1.33 and indicates high-risk prediction. D, Patient with T4 stage shows f(x) = 1.31 and indicates high-risk prediction. MTD, maximum tumor diameter; SII, systemic immune-inflammation index; PLR, platelet-to-lymphocyte ratio; PAR, platelet-to-albumin ratio; Lauren type, Lauren histological classification (poorly); FIB, fibrinogen; CEA, carcinoembryonic antigen.

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