An ensemble belief rule base model for pathologic complete response prediction in gastric cancer

It is well known that the decision-making on treating gastric cancer is usually the summary of several experts’ advice. Moreover, the interpretability and reliability of a model used to assist doctors with final decisions are very important in gastric cancer. Thus, this paper designed an ensemble belief rule base (EnBRB) model to ensemble multiple BRB models and predict the statement of pathological complete response (pCR), aiming to simulate the expert consultation widely used for clinical decisions on gastric cancer. In EnBRB, five BRB models were built individually using experts’ knowledge and trained through patient treatment information. Furthermore, the final output was calculated via the evidential reasoning (ER) based ensemble strategy on the results of each BRB for a more reliable decision. Then, to improve the model’s performance, a two-stage approach with a differential evolution (DE) algorithm was designed to enhance the performance of EnBRB. The experimental results demonstrated that a higher accuracy (0.9296±0.0521) and AUC (0.9570±0.0368) were obtained by our EnBRB. Moreover, the difference between sensitivity and specificity achieved by EnBRB was smaller than other comparison methods.