Table 3

Comparison of FPG, 2hPG and metabolites optimized machine learning performance, indicating greatest performance in the metabolite model

SetsParametersOptimized machine learner algorithmAUC*SeSpAccuracyPF scoreBest model score (F score plus AUC)
TrainingFPGLR0.724 (0.645–0.803)60.00%75.00%67.50%70.60%64.90%1.373
2hPGLR0.726 (0.648–0.804)58.75%72.50%65.63%68.12%63.09%1.3569
Metabolite modelDT0.830 (0.765–0.894)86.30%68.80%77.50%73.40%79.30%1.623
TestingFPGLR0.706 (0.596–0.816)57.10%66.70%61.90%63.20%60.00%1.306
2hPG ModelLR0.661 (0.543–0.779)57.10%71.40%64.30%66.70%61.50%1.276
Metabolite modelDT0.769 (0.667–0.871)73.80%69.10%71.40%70.50%72.10%1.490
Glucose model (FPG and 2hPG)DT0.73288.10%47.60%67.90%62.70%73.30%1.465
Combined modelNB0.75454.80%76.20%65.50%69.70%61.30%1.367
  • DT, J48 decision tree; LR, logistic regression, NB, naive Bayes.

  • *Data are presented as the mean and 95% CI.