Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma

Lymph node metastasis (LNM) constitutes one of the main prognostic factors for long-term survival in endometrial carcinoma (EC). However, the previous studies on LNM diagnosis failed to consider both model interpretability and class imbalance. In this study, the extended belief rule base (EBRB) expert system is introduced to develop a novel EBRB-based LNM diagnosis model. First, the interpretability of the EBRB expert system is investigated to demonstrate the feasibility on LNM diagnosis; Second, imbalanced learning is introduced to improve rule generation scheme for constructing base EBRBs; Third, by considering the trust of base EBRBs and base diagnoses, ensemble learning is introduced to improve rule inference scheme for diagnosing final LNM. In the case study, real EC patient data collected from Fujian Provincial Maternity and Children's Hospital are used to verify the effectiveness of the proposed EBRB-based model by comparing with the variants of rule generation schemes and rule inference schemes, as well as some machine learning-based LNM diagnosis models. The comparative results showed that the proposed EBRB-based model has better sensitivity, specificity, and geometric mean in diagnosing LNM for EC patients.