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Table 3 Performance of classification models for identifying parameters that can be classified with clinical malaria

From: Machine learning approaches classify clinical malaria outcomes based on haematological parameters

ANN UM vs SM vs nMI UM vs nMI SM vs nMI
Model type Multi-classification model Binary model Binary model
Data splitting
 Total data (100%) n = 2,207 n = 1,681 n = 1,504
 Training and validation data (80%) n = 1,766 n = 1,345 n = 1,204
 Testing data (20%) n = 441 n = 336 n = 300
Training performance
 Training accuracy 0.862 0.856 0.985
 Training loss 0.396 0.425 0.062
 Validation accuracy 0.828 0.842 0.978
 Validation loss 0.432 0.434 0.102
Testing performance
 Testing accuracy 0.853 0.801 0.96
 Kappa 0.768 0.583 0.913
 ROC_AUC NAa 0.866 0.983
 Precision 0.855 0.780 0.971
 Recall 0.856 0.717 0.918
 F1 score 0.856 0.747 0.944
  1. Training and cross-validation accuracy as well as testing accuracy, area under the ROC curve (AUC), precision, recall, and F1 score. Multiclass analysis among all three-disease conditions, training accuracy was 0.862 with 0.828 validation accuracy. The model classified the three classes with 0.853 test accuracy. The ANN (UM vs nMI) had an accuracy of ≥ 0.801 for training, validation, and testing accuracy. The ANN (SM vs nMI) had the highest classification accuracy of ≥ 0.96
  2. aWe did not generate ROC-AUC for multi-classification models