Fig. 4: Model performance, feature importance and external validation of the machine learning model for pCR prediction. | npj Breast Cancer

Fig. 4: Model performance, feature importance and external validation of the machine learning model for pCR prediction.

From: Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy

Fig. 4

a Coefficients of the Elastic Net model. Positive coefficients are plotted clockwise and negative counterclockwise. Age: - 0.036, cN: -0.056, G: 0.059, PR: -0.073, ER: -0.075, cT: -0.075, Ki67: 0.095. b Barplot for SGD Elastic Net permutation importance coefficients. Weights are presented with respective errors. c Barplot of mean SHAP values. The barplot represents the mean global feature importance. The most important features in the dataset were cT and Ki67. d Beeswarn plot for SHAP values. Each patient is represented by a single dot on each feature row. The position of the dot is determined by the SHAP value. Positive SHAP values indicates greater probability of pCR. e SHAP heatmap. In the SHAP heatmap patients are reported on the x-axis and features on the y-axis with SHAP values encoded on a color scale. Patients are ordered in descending order based on the model predicted output and the global importance of each feature is shown on the left barplot. f SHAP decision plot. SHAP decision plots show how the model arrives at its predictions. Each line represents a single patient with corresponding SHAP values for each feature.

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