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Fig. 2 | European Journal of Medical Research

Fig. 2

From: The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models

Fig. 2

Feature selection analyzed by Boruta algorithm. The horizontal axis is the name of each variable, and the vertical axis is the Z-value of each variable. The box plot shows the Z-value of each variable in the model calculation. The green boxes represent the 76 important variables, the yellow represents tentative attributes, and the red represents unimportant variables. los_icu length of stay in intensive care unit, scr serum creatinine, eGFR estimated glomerular filtration rate, CKD chronic kidney disease, ACS acute coronary syndrome, HT hypertension, PCI percutaneous coronary intervention, CABG coronary artery bypass grafting, NOAC Non-vitamin K Antagonist Oral Anticoagulant, CRRT continuous renal replacement therapy, max maximum, min minimum, WBC white blood cell, RBC red blood cell, ALT alanine aminotransferase, AST aspartate aminotransferase, ALP alkaline phosphatase, BUN blood urea nitrogen, INR International Normalized Ratio, PT prothrombin time, PTT partial thromboplastin time, SOFA sequential organ failure assessment, sbp systolic blood pressure, dbp diastolic blood pressure, mbp mean blood pressure, HR heart rate, spo2 oxyhemoglobin saturation

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