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Table 4 Performance of the conventional and conventional+GRS models in predicting dyslipidaemia

From: Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

  AUC AUC Continuous NRI, % IDI, %
Cox
 Conventional model 0.702(0.673, 0.729)    
 Conventional+GRS model 0.707(0.679, 0.734) 0.0049(P = 0.0549) 25.6 (13.8, 35.8)* 2.3 (1.1, 3.7)*
ANN
 Conventional model 0.736(0.708, 0.762)    
 Conventional+GRS model 0.754(0.727, 0.779) 0.0183(P = 0.0031)* 7.8 (−2.7, 18.5) 1.0 (−0.3, 2.4)
RF
 Conventional model 0.787 (0.762, 0.811)    
 Conventional+GRS model 0.810 (0.762, 0.811) 0.0230(P = 0.023)* 14.1 (1.1, 26.1)* 2.5 (0.5, 4.2)*
GBM
 Conventional model 0.816(0.792, 0.839)    
 Conventional+GRS model 0.831(0.808, 0.853) 0.0151(P = 0.0135)* 18.1 (4.4, 27.2)* 1.8 (0.1, 3.5)*
  1. Abbreviations: AUC area under receiver operating characteristic curve, AUC difference between AUC of conventional model and conventional+GRS model, NRI net reclassification improvement, IDI integrated discrimination improvement, ANN artificial neural network, RF random forest, GBM gradient boosting machine
  2. *Statistically significant values, P < 0.05
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