title: Application and Comparison of Laboratory Parameters for Forecasting Severe Hand-Foot-Mouth Disease Using Logistic Regression, Discriminant Analysis and Decision Tree.
abstract: BACKGROUND
  In recent years, the prevalence of hand-foot-mouth disease (HFMD) in China and some other countries has caused worldwide concern. Mild cases tend to recover within a week, while severe cases may progress rapidly and tend to have bad outcome. Since there is no vaccine for HFMD and anti-inflammatory treatment is not ideal. In this study, we aimed to establish a valid forecasting model for severe HFMD using common laboratory parameters.
  
  
  METHODS
  Retrospectively, 77 severe HFMD cases from Zhengzhou Children's hospital in the peaking period between years 2013 to 2015 were collected, with 77 mild HFMD cases in the same area. The study recorded common laboratory parameters to assist in establishment of the severe HFMD model. After screening the important variables using Mann-Whitney U test, the study also matched the logistic regression (LR), discriminant analysis (DA), and decision tree (DT) to make a comparison.
  
  
  RESULTS
  Compared with that of the mild group, serum levels of WBC, PLT, PCT, MCV, MCH, LCR, SCR, LCC, GLO, CK-MB, K, S100, and B in the severe group were higher (p < 0.05), while MCR, EOR, BASOR, SCC, MCC, EO, BASO, NA, CL, T, Th, and Th/Ts were lower (p < 0.05). Five indicators including MCR, LCC, Th, CK-MB, and CL were screened out by LR and the same for DA, and five variables including EO, LCC, CL, GLO, and MCC screened out by DT. The area under the curve (AUC) of LR, DA, and DT was 0.805, 0.779 and 0.864, respectively.
  
  
  CONCLUSIONS
  The findings were that common laboratory indexes were effectively used to distinguish the mild HFMD cases and severe HFMD cases by LR, DA, and DT, and DT had the best classification effect with an AUC of 0.864.
authors.forename: Meili
authors.initials: M
authors.lastname: Sui
authors.forename: Xueyong
authors.initials: X
authors.lastname: Huang
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaomei
authors.initials: X
authors.lastname: Ma
authors.forename: Chao
authors.initials: C
authors.lastname: Zhang
authors.forename: Xingle
authors.initials: X
authors.lastname: Li
authors.forename: Zhijuan
authors.initials: Z
authors.lastname: Chen
authors.forename: Huifen
authors.initials: H
authors.lastname: Feng
authors.forename: Jingchao
authors.initials: J
authors.lastname: Ren
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.forename: Bianli
authors.initials: B
authors.lastname: Xu
authors.forename: Guangcai
authors.initials: G
authors.lastname: Duan
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D057225:Data Mining; D003661:Decision Support Techniques; D003663:Decision Trees; D016002:Discriminant Analysis; D005260:Female; D005536:Foot-and-Mouth Disease / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology* / Q000821:virology; D005544:Forecasting; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D013997:Time Factors
chemical_list: D015415:Biomarkers