Model Explainability
HEAT
In this study, LIME was used to assess the model explainability for the optimal model generated by GBDT. LIME was able to rank feature importance and visualize their contributions to medical disputes on an individual case basis. The top 10 features for each case were identified (In this study, a heat map was used to visualize the SHAP values of the first 1000 participants in the internal (Figure S5A in Model explainability using local interpretable model-agnostic explanation: (A) a true positive case and (B) a true negative case. In the first case, the study depicted a specific individual with a high probability of experiencing medical dispute (64%), whereas the second case showed low risk of medical dispute (21%). Features with an orange bar imply contributory elements to increase medical dispute, whereas those with a cyan bar indicate protective features.