Fig 1.
Flow chart of pattern recognition and cluster analysis based variable selection process for late-onset preeclampsia prediction.
Table 1.
Maternal characteristics and laboratory parameters at early second trimester.
Table 2.
Clinical characteristics and laboratory parameters at delivery.
Fig 2.
Normalized importance of the selected variables for late-onset preeclampsia prediction models.
The plot shows relative importance of the variables in random forest model. IncNodePurity reflects the reduction in entropy, which is the uncertainty, due to sorting of the attribute. Abbreviation: SBP, systolic blood pressure; WBC, white blood cell; UPCR, urine protein to creatinine ratio; UACT, urine albumin to creatinine ratio.
Fig 3.
Receiver operating characteristic curves of late-onset preeclampsia prediction models.
C-statistics for each prediction model are presented in the graph. Abbreviation: DT, decision tree; NBC, naïve Bayes classification; SVM, support vector machine; RF, random forest; SGB, stochastic gradient boosting; LR, logistic regression.
Table 3.
Comparison of prediction performances for late-onset preeclampsia development.