Fig. 4: Performance of machine learning algorithms for classification. | Communications Biology

Fig. 4: Performance of machine learning algorithms for classification.

From: Brain connectome gradient dysfunction in patients with end-stage renal disease and its association with clinical phenotype and cognitive deficits

Fig. 4: Performance of machine learning algorithms for classification.The alternative text for this image may have been generated using AI.

a ROC curve of the classifiers. The accuracy and AUC of logistic regression were 84.8% and 0.901, respectively, which was better than those of the other classifier algorithms. b Features correlation heatmap. ROC receiver operating characteristic, AUC area under the curve, KNN k-nearest neighbor, SVM support vector machine, DT decision tree, RF random forest, GBDT gradient boosting decision tree.

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