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.