Table 4.

The ablation experiments of the second predictive model.

	Accuracy (%)	AUC (%)	95% CI for AUC (%)	Sensitivity (%)	95% CI for sensitivity (%)	Specificity (%)	95% CI for specificity (%)	F1 (%)
hs-CRP	68.75	81.28	71.2–91.27	66.67	50.95–79.56	72.00	50.40–87.13	73.17
+IL-6	74.29	79.56	68.92–90.19	80.00	64.95–89.91	64.00	42.62–81.29	80.00
+IL-2R	75.71	83.38	74.00–92.76	77.78	62.52–88.29	72.00	50.41–87.12	80.46
+PCT	75.71	79.56	68.96–90.15	75.56	60.14–86.61	76.00	54.48–89.84	80.00
+TNF-α	74.29	83.91	74.60–93.22	71.11	55.48–83.16	80.00	58.87–92.39	78.05
+LYMPHA	78.57	87.38	78.88–95.88	73.33	57.79–84.90	88.00	67.66–96.85	81.48
+NEUTP	80.00	90.58	83.59–97.56	73.33	57.79–84.90	92.00	72.50–98.60	82.50
+RBC	82.86	92.98	86.91–99.04	82.22	67.41–91.49	84.00	63.08–94.75	86.05

The first experiment only used the hs-CRP value as a single input feature of the model. Then IL-6 was added to the second column, that is, the model used hs-CRP and IL-6 as input features. Finally, when taking eight features, including Red Blood Cell Count (RBC), the model’s performance is very close to the prediction model with 45 features.