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Fig. 6 | BMC Medicine

Fig. 6

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

Fig. 6

Platelet 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 volume

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