Fig. 4

Global and local interpretations of model predictions

(A) SHAP plot shows the feature importance in our model. 1D stretch, biomarker score, ischemic cardiomyopathy, QOL score, and age were indicated as the top 5 most important features for determining patient response probability. The scatter width and separation indicate the feature importance, and the color indicates which direction of that feature value is predictive of high vs. low patient response. (B) LIME plot shows the most significant contributing features for an example responder wherein a 1D Stretch of ≤ 1.08 along with a lack of RBBB, atrial flutter, ischemic cardiomyopathy, AT_PSVT, PAF, and SA surgery increased the probability of responding favorably to CRT treatment. (C) LIME plot shows the most significant contributing features for an example non-responder wherein a 1D Stretch of > 1.14 along with a lack of VT-SVT, Afib, and nonsustained VT increased the probability of not responding to treatment. On the other hand, a history of ischemic cardiomyopathy also affected the predicted non-response to CRT.