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Fig. 5 | BMC Medicine

Fig. 5

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

Fig. 5

ROC curve for classification of SM was near perfect. The ROC curve plots sensitivity versus specificity for all possible cut-offs. Each point on the curve represents a different cut-off value, which is connected to form a curve. The diagonal line is a reference line for the ROC curve. a ROC for the ANN (UM vs nMI) with an area under the curve (AUC) of 0.866 which is basically an average of true positive rate across all possible false positive rates. b ROC for the ANN (SM vs nMI) is right angled which means its near perfect with an AUC of 0.983. The levels of AUC indicate a good performance of the models in classifying UM and SM

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