Value of Cytokine Expression in Early Diagnosis and Prognosis of Tumor Metastasis

Figure 4

(a) Random forest is the best machine learning algorithm for predicting tumor metastasis. The model with the random forest algorithm showed the highest accuracy (0.84) and its kappa index was 0.38. (b) The relationship between variables and accuracy in the random forest predictor, with the repeated five-fold cross validation. (c) The variable importance rank in the random forest model are lymphocyte percentage, IL-6, IL-12p70, and IL-8. (d) The AUC value of ROC curves is 0.885, which indicated that the random forest model was robust for predicting tumor metastasis. Abbreviation: RF, random forests; LDA, linear discriminant analysis; SVM, support vector machine, KNN, K-nearest neighbours; CART, classification and regression tree; LY, lymphocyte.

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