Fig. 2

Application of Machine Learning in Feature Selection. A Shapley Additive Explanations (SHAP) for the random forest model. A Distribution of the impact of each feature on the model output. Each dot represents a patient in a row. The colors of the dots represent the feature values: red represents larger values and blue represents lower values. B Feature selection for the relationship between various TyG indices and in-hospital mortality was analyzed using the Boruta algorithm. B The horizontal axis shows the name of each variable, whereas the vertical axis represents the Z-value of each variable. The box plot depicts the Z-value of each variable in the model calculation, with green boxes representing important variables, blue boxes representing tentative attributes, and yellow boxes representing unimportant variables. SOFA: Sequential Organ Failure Assessment; Activated Partial Thromboplastin Time WBC: White Blood Cell Count BMI: Body Mass Index LDL: Low-Density Lipoprotein PT: Prothrombin Time ALP: Alkaline Phosphatase RDW: Red Cell Distribution Width HDL: High-Density Lipoprotein. TyG: triglyceride glucose index ALT: Alanine Aminotransferase; COPD: Chronic Obstructive Pulmonary Disease CAD: Coronary Artery Disease; CHF: Congestive Heart Failure; HTN: hypertension