Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children

Figure 1. Relative importance (RI) for each individual predictor. Note that the *indicated the level of significance, p-value < 0.001. MRI-related predictors were in yellow and clinical predictors were in blue.

Figure 2. The interaction effect between two predictors. The x-axis denotes the status of the predictor. The y-axis (deviance) is a quality-of-fit statistic for the interaction that measures how well the new configuration fits the data. (In Fig. 2A, we can observe a significant increase in the risk of severe HFMD when the probability of increased WBC count and hyperglycemia changed from 0.5 to 1. In Fig. 2B, we can observe that conditioning on the change of spinal cord involvement, the effect of duration of fever ≥3 days is minor. In Fig. 2C, when the age is between 0 and 50 months and the patient is male, we can observe a significant increase risk of severe HFMD. In Fig. 2D, we can observe that if we fix the body temperature at 38 degree, then there is a slight increase in the risk of severe HFMD when the probability of brainstem involvement changed from 0 to 1. However, when the body temperature is between 40 and 41 degree, we can observe a significant increase in the risk of severe HFMD if the status of brainstem involvement changed. In Fig. 2E, when the body temperature is between 40 and 41 degree and the age is between 0 and 50 months, we can observe a significant increase risk of…