AutoBRB: An automated belief rule base model for pathologic complete response prediction in gastric cancer

Gastric cancer is one of the most severe malignant lesions. Neoadjuvant chemotherapy (NAC) has proven to be an effective method in gastric cancer treatment, and patients who achieved the pathologic complete response (pCR) after NAC can improve survival time further. To accurately predict pCR in an interpretable way, a new automated belief rule base (AutoBRB) model is developed with careful data analysis in this paper. In AutoBRB, to determine the referential values that are important for the rule building, both the information gain ratio and expert knowledge are used, while a table-based strategy is designed to initialize the belief degrees for each rule. Then, the differential evolution (DE) algorithm is employed and modified for model optimization to improve the model's performance. Finally, with the help of training data, an adaptive searching strategy is designed to set the confidence threshold for the final prediction. The experimental results demonstrate that AutoBRB shows a more reasonable performance on the prediction of pCR.