Fig. 4: Statistical analysis of features included in models.
From: Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
a Heatmap representing the correlation between continuous features included in MRPMC using Spearman’s correlation coefficient. The colors in the plot represent the correlation coefficients. The redder the color, the stronger the positive monotonic relationship. The bluer the color, the stronger the negative monotonic relationship. The size of the circle represents the absolute value of the correlation coefficient, where a larger circle represents a stronger correlation. The numbers in the lower triangle represent the value of correlation coefficient. b Scaled importance rank of all features included in MRPMC for identifying high mortality risk COVID-19 patients included in the models. The size of circles represents the value of relative importance. The different color of circles represents the feature importance in different models. c Box and jitter plots showing distribution of continuous features included in MRPMC between deceased patients (n = 254) and discharged patients (n = 1906). The center line represents the median of the feature. Box limits represent upper and lower quartiles. Whiskers represent 1.5 times interquartile range. Gray points represent outliers. The median [IQR] of the features shown in Fig. 4c were listed in Supplementary Table 4. Wilcoxon test was used in the univariate comparison between groups and a two-tailed p < 0.05 was considered as statistically significant. ***p < 0.001. Source data are provided as a Source Data file. MRPMC mortality risk prediction model for COVID-19, ALB albumin, SpO2 oxygen saturation, BUN blood urea nitrogen, RR respiratory rate, LYM lymphocyte count, PLT platelet count, No. comorbidities number of comorbidities, CKD chronic kidney disease, IQR interquartile range.
