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Table 3 Area under the curve, accuracy, and the three most important predictors for the prediction of large for gestational age (LGA) birth using logistic regression and five machine learning methods pre-pregnancy and at 26 weeks in nulliparous and multiparous women

From: Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study

 

Pre-pregnancy

26 weeks

LR

EN

CT

RF

GB

NN

LR

EN

CART

RF

GB

NN

LGA - Primiparae

 Area under the curve

0.592

0.587

0.563

0.576

0.587

0.594

0.702

0.705

0.675

0.673

0.697

0.705

 Accuracy

0.826

0.827

0.800

0.824

0.832

0.827

0.843

0.834

0.780

0.834

0.839

0.842

 Maternal age

         

 Common-law/married

           

 Pre-pregnancy smoking

  

      

 Pre-pregnancy BMI

 

 

 Pre-existing diabetes

 

 

 

 

 

 Weight gain at 26 wks

      

 

 Smoking in pregnancy

        

 

 Pregnancy-induced hypertension

         

 

 Gestational diabetes

      

     

LGA - Multiparae

 Area under the curve

0.700

0.700

0.659

0.692

0.704

0.700

0.745

0.748

0.718

0.728

0.748

0.746

 Accuracy

0.807

0.806

0.817

0.795

0.804

0.807

0.813

0.809

0.794

0.799

0.805

0.812

 Maternal age

           

 Pre-pregnancy smoking

  

 

       

 Pre-pregnancy BMI

 

  

 

 Pre-existing diabetes

 

 

 

 

   

 Previous LBW infant

            

 Previous infant > 4080 g

 Previous death of neonate ≥500 g

     

     

 Weight gain at 26 wks

      

 

 Smoking in pregnancy

        

   
  1. Abbreviations: BMI body mass index, CT classification tree, EN elastic net, GB Gradient boosting, LBW low birth weight, LR logistic regression, NN neural network, RF random forest, wks weeks