title: [Application of Bayes Probability Model in Differentiation of Yin and Yang Jaundice Syndromes in Neonates].
abstract: OBJECTIVE
  To study the application of Bayes probability model in differentiating yin and yang jaundice syndromes in neonates.
  
  
  METHODS
  Totally 107 jaundice neonates who admitted to hospital within 10 days after birth were assigned to two groups according to syndrome differentiation, 68 in the yang jaundice syndrome group and 39 in the yin jaundice syndrome group. Data collected for neonates were factors related to jaundice before, during and after birth. Blood routines, liver and renal functions, and myocardial enzymes were tested on the admission day or the next day. Logistic regression model and Bayes discriminating analysis were used to screen factors important for yin and yang jaundice syndrome differentiation. Finally, Bayes probability model for yin and yang jaundice syndromes was established and assessed.
  
  
  RESULTS
  Factors important for yin and yang jaundice syndrome differentiation screened by Logistic regression model and Bayes discriminating analysis included mothers' age, mother with gestational diabetes mellitus (GDM), gestational age, asphyxia, or ABO hemolytic diseases, red blood cell distribution width (RDW-SD), platelet-large cell ratio (P-LCR), serum direct bilirubin (DBIL), alkaline phosphatase (ALP), cholinesterase (CHE). Bayes discriminating analysis was performed by SPSS to obtain Bayes discriminant function coefficient. Bayes discriminant function was established according to discriminant function coefficients. Yang jaundice syndrome: y1= -21. 701 +2. 589 x mother's age + 1. 037 x GDM-17. 175 x asphyxia + 13. 876 x gestational age + 6. 303 x ABO hemolytic disease + 2.116 x RDW-SD + 0. 831 x DBIL + 0. 012 x ALP + 1. 697 x LCR + 0. 001 x CHE; Yin jaundice syndrome: y2= -33. 511 + 2.991 x mother's age + 3.960 x GDM-12. 877 x asphyxia + 11. 848 x gestational age + 1. 820 x ABO hemolytic disease +2. 231 x RDW-SD +0. 999 x DBIL +0. 023 x ALP +1. 916 x LCR +0. 002 x CHE. Bayes discriminant function was hypothesis tested and got Wilks' λ =0. 393 (P =0. 000). So Bayes discriminant function was proved to be with statistical difference. To check Bayes probability model in discriminating yin and yang jaundice syndromes, coincidence rates for yin and yang jaundice syndromes were both 90% plus.
  
  
  CONCLUSIONS
  Yin and yang jaundice syndromes in neonates could be accurately judged by Bayesian discriminating functions.
authors.forename: Chun-sun
authors.initials: CS
authors.lastname: Mu
authors.forename: Ping
authors.initials: P
authors.lastname: Zhang
authors.forename: Chun-yan
authors.initials: CY
authors.lastname: Kong
authors.forename: Yang-ning
authors.initials: YN
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D006760:Hospitalization; D006801:Humans; D007231:Infant, Newborn; D007565:Jaundice / Q000175:diagnosis*; D008516:Medicine, Chinese Traditional; D015233:Models, Statistical; D011336:Probability; D013577:Syndrome