title: Predicting cesarean delivery with decision tree models.
abstract: OBJECTIVE
  The purpose of this study was to determine whether decision tree-based methods can be used to predict cesarean delivery.
  
  
  METHODS
  This was a historical cohort study of women delivered of live-born singleton neonates in 1995 through 1997 (22,157). The frequency of cesarean delivery was 17%; 78 variables were used for analysis. Decision tree rule-based methods and logistic regression models were each applied to the same 50% of the sample to develop the predictive training models and these models were tested on the remaining 50%.
  
  
  RESULTS
  Decision tree receiver operating characteristic curve areas were as follows: nulliparous, 0.82; parous, 0.93. Logistic receiver operating characteristic curve areas were as follows: nulliparous, 0.86; parous, 0.93. Decision tree methods and logistic regression methods used similar predictive variables; however, logistic methods required more variables and yielded less intelligible models. Among the 6 decision tree building methods tested, the strict minimum message length criterion yielded decision trees that were small yet accurate. Risk factor variables were identified in 676 nulliparous cesarean deliveries (69%) and 419 parous cesarean deliveries (47.6%).
  
  
  CONCLUSIONS
  Decision tree models can be used to predict cesarean delivery. Models built with strict minimum message length decision trees have the following attributes: Their performance is comparable to that of logistic regression; they are small enough to be intelligible to physicians; they reveal causal dependencies among variables not detected by logistic regression; they can handle missing values more easily than can logistic methods; they predict cesarean deliveries that lack a categorized risk factor variable.
authors.affiliation: Department of Obstetrics, Gynecology, and Reproductive Sciences, Magee Womens Research Institute, University of Pittsburgh School of Medicine, Pennsylvania 15213, USA.
authors.forename: C J
authors.initials: CJ
authors.lastname: Sims
authors.forename: L
authors.initials: L
authors.lastname: Meyn
authors.forename: R
authors.initials: R
authors.lastname: Caruana
authors.forename: R B
authors.initials: RB
authors.lastname: Rao
authors.forename: T
authors.initials: T
authors.lastname: Mitchell
authors.forename: M
authors.initials: M
authors.lastname: Krohn
mesh_terms: D000293:Adolescent; D000328:Adult; D002585:Cesarean Section; D015331:Cohort Studies; D003663:Decision Trees; D005260:Female; D005544:Forecasting; D006801:Humans; D011247:Pregnancy; D012044:Regression Analysis; D012307:Risk Factors