Fig. 6From: Machine learning approaches classify clinical malaria outcomes based on haematological parametersPlatelet and RBC counts identified as classifiers of both UM and SM. The Keras model was explained using local interpretable model-agnostic explanations (LIME Package in R-software). The explainer results of the test data, which are represented as feature weights, were extracted from the explainer and used to plot the heatmaps to show a consolidated picture of the importance of each haematological parameter. The weights that are < − 0.1 indicate that they are low during UM or SM. a The heatmap shows that platelet, RBC, and lymphocyte percentages/counts can classify UM and b shows the haematological parameters that can classify SM, and they include RBC counts, platelet counts, mean platelet volume, and mean cell volumeBack to article page