TY - JOUR
T1 - Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
AU - Zhang, Bin
AU - Wan, Xiang
AU - Ouyang, Fu Sheng
AU - Dong, Yu Hao
AU - Luo, De Hui
AU - Liu, Jing
AU - Liang, Long
AU - Chen, Wen Bo
AU - Luo, Xiao Ning
AU - Mo, Xiao Kai
AU - Zhang, Lu
AU - Huang, Wen Hui
AU - Pei, Shu Fang
AU - Guo, Bao Liang
AU - Liang, Chang Hong
AU - Lian, Zhou Yang
AU - Zhang, Shui Xing
N1 - Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85024390108&partnerID=8YFLogxK
U2 - 10.1038/s41598-017-05505-8
DO - 10.1038/s41598-017-05505-8
M3 - Journal article
C2 - 28710409
AN - SCOPUS:85024390108
SN - 2045-2322
VL - 7
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 5368
ER -