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Fig. 2 | European Journal of Medical Research

Fig. 2

From: Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach

Fig. 2

Feature importance for models using pre-procedural, procedural, and post-procedural features. A Logistic Regression Coefficients; B Random Forest Feature Importance; C CatBoost Feature Importance. LVEF left ventricular ejection fraction, FPG fasting plasma glucose, CPR cardiopulmonary resuscitation, PCI percutaneous coronary intervention, BMI body mass index, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, PMH past medical history, UA unstable angina, MI myocardial infarction, TG triglyceride, HDL-C low-density lipoprotein cholesterol, CAD coronary artery disease, CABG coronary artery bypass grafting

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