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Table 3 Model performance evaluation with test data set, “A case study performed in 2019-20 to develop a Machine Learning-algorithm to estimate the incidence of Diabetes Mellitus in France”

From: Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

  LDA LR FDA C5
Accuracy 0,67 0,65 0,66 0,64
95% CI: (0,66-0,68) (0,64-0,66) (0,65-0,67) (0,63-0,65)
No Information Rate: 0,998 0,998 0,998 0,998
P-Value [Acc > NIR]: 1000 1000 1000 1000
Kappa 0,003 0,004 0,002 0,003
McNemar’s Test P-Value <2e-16 <2e-16 <2e-16 <2e-16
Sensitivity 0,625 0,750 0,563 0,625
Specificity 0,673 0,650 0,661 0,640
Pos Pred Value 0,003 0,004 0,003 0,003
Neg Pred Value 0,999 0,999 0,999 0,999
F1-statistics 2,50 3,0 2252 2,50
Detection Rate 0,001 0,001 0,001 0,001
Balanced Accuracy 0,649 0,700 0,612 0,633