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Fig. 2 | BMC Medical Informatics and Decision Making

Fig. 2

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. 2

Correlation analysis of the variables in the dataset to predict NR, as shown in the heat map, the dataset was not composed of many correlated variables, which indicated the model was simpler and more stable. 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; PCI, percutaneous coronary intervention; 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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