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Fig. 2 | BMC Neurology

Fig. 2

From: Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts

Fig. 2

Final ensemble neural network model. A Visual representation of the final ensemble neural network model developed in the training set, which was comprised of five individual sub-models. The synaptic weights and node bias terms associated with each of the five sub-models are indicated. All input values were scaled between 0 and 1 for training. Sigmoid activation functions were used at all nodes. Final ensemble stroke prediction probabilities were generated via simple averaging of the sub-model prediction probabilities. Pr, probability. B The relative importance of each cell type in each of the five individual sub-models which comprise the final ensemble neural network model, as calculated using the Olden method. Averaged importance values for each cell type across the five sub-models are also indicated. Values of 0 indicate little importance, while values approaching 1 and -1 indicate strong positive and negative associations with stroke prediction probability respectively. C ROC curves depicting the ability of the final ensemble neural network model to discriminate between stroke patients and controls in the training set, test set, and total study population. Sensitivity and specificity values are indicated for the diagnostic cutoff which produced the highest Youden index. Bootstrapped 95% confidence intervals associated with all diagnostic statistics are indicated. P-values indicate the probability area under ROC curve values differ from 0.5. Training set and test set AUC values were statistically compared using the DeLong method. *Statistically significant

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