Fig. 2
From: Comparing different machine learning techniques for predicting COVID-19 severity
The heat map of correlation between features. Color indicates the value of the correlation coefficient (r). The color intensity is proportional to the correlation coefficient (r), with positive correlations (r > 0) shown and negative correlations (r < 0), lower intensive color indicates lower correlations, in the study, 23 features selected by machine learning techniques, each feature is weakly correlated with each other (r < 0.4). Twenty-three features including C-CT, fever, MT, HR, SBP, HB, NLR, RDW-CV, IMG, IL-6, ESR, IBIL, ALP CysC, LDH, Ca2+, AFU, AMS, RBP, Hs-cTn, MB, PT(s), D-dimer. C-CT chest computed tomography, MT malignant tumor, HR heart rate, SBP systolic blood pressure, NLR neutrophil-to-lymphocyte ratio, HB hemoglobin concentration, RDW-CV red cell volume distribution width, LDH lactate dehydrogenase, IBIL indirect bilirubin, PT prothrombin time, ESR erythrocyte sedimentation rate, AFU α-fucosidase, RBP retinol-Binding protein, IL-6 interleukin-6, Hs-cTn hypersensitive troponin, AMS amylase, CysC cystatin C, IMG immature granulocyte, ALP alkaline phosphatas, MB myoglobin