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Fig. 4 | BMC Medical Informatics and Decision Making

Fig. 4

From: Development and validation of a machine learning-based model to assess probability of systemic inflammatory response syndrome in patients with severe multiple traumas

Fig. 4

Feature importance analysis using the SHapley Additive exPlanation (SHAP). SHAP feature importance analysis of the internal validation cohort. The X-axis of the graph represents the impact of the feature on the prediction result. The Y-axis represents the model predictors. The higher the feature on the graph, the stronger is the correlation between the feature and the prediction result. Blue indicates a low feature value, whereas pink indicates a high feature value. The top 15 features are as follows: WBC, leukocyte count; TEMP, fever; oWBC, excess leukocyte count; AIS_AB, weighted AIS severity for abdominal injuries; MCHC, mean corpuscular hemoglobin concentration; PRT, serum total protein; URA, serum urea; AIS_HD, weighted AIS severity for head injuries; NISS, new injury severity score; AIS_LE, weighted AIS severity for lower extremity injuries; MCH, mean corpuscular hemoglobin; FBG, fibrinogen; GLU, serum glucose; and MCV, mean corpuscular volume

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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