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

Bin Zhang, Xiang WAN, Fu Sheng Ouyang, Yu Hao Dong, Dehui LUO, Jing Liu, Long Liang, Wen Bo Chen, Xiao Ning Luo, Xiao Kai Mo, Lu Zhang, Wen Hui Huang, Shu Fang Pei, Bao Liang Guo, Chang Hong Liang, Zhou Yang Lian*, Shui Xing Zhang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

15 Citations (Scopus)


The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 109/L (RI: 49.47, p < 0.001) was the top predictor of severe HFMD, followed by spinal cord involvement (RI: 26.62, p < 0.001), spinal nerve roots involvement (RI: 10.34, p < 0.001), hyperglycemia (RI: 3.40, p < 0.001), and brain or spinal meninges involvement (RI: 2.45, p = 0.003). Interactions between elevated WBC count and hyperglycemia (H statistic: 0.231, 95% CI: 0-0.262, p = 0.031), between spinal cord involvement and duration of fever ≥3 days (H statistic: 0.291, 95% CI: 0.035-0.326, p = 0.035), and between brainstem involvement and body temperature (H statistic: 0.313, 95% CI: 0-0.273, p = 0.017) were observed. Therefore, GBT is capable to identify the predictors for severe HFMD and their interaction effects, outperforming conventional regression methods.

Original languageEnglish
Article number5368
JournalScientific Reports
Issue number1
Publication statusPublished - 1 Dec 2017

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