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Fig. 5 | BMC Medical Informatics and Decision Making

Fig. 5

From: Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention

Fig. 5

Ranking of feature importance to predict NR: Contribution of clinical, laboratory, and angiographic variables to the optimal AI-based prediction model (RAN). ALB, albumin; Cr, creatinine; CK-MB, creatine kinase-MB; cTnI, cardiac troponin I; DM, diabetes mellitus; D-to-B, door-to-balloon; EF, ejection fraction; FIB, Fibrinogen; Glu, glucose; Hb, hemoglobin; HBP, high blood pressure; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; MA, Malignant arrhythmia; PCI, percutaneous coronary intervention; RAN, random forest; SO-to-FMC, symptom-onset to-first medical contact; TC, total cholesterol; TG, triglyceride; TIMI, thrombolysis and thrombin inhibition in myocardial infarction; UA, uric acid; WBC, white blood cells

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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