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The precise diagnosis and scientific management of diabetes are highly important for improving patients' quality of life and reducing the risk of complications. However, in actual clinical settings, diagnostic processes often face challenges, including significant individual differences among patients, complex and diverse parameters, and heterogeneity in disease progression. These challenges not only impose greater requirements on the adaptability and precision of diagnostic and therapeutic models but also highlight the need for explainable disease mechanisms and rational treatment strategies. To address these issues, this study proposes an Extended Belief Rule Base (EBRB) model based on neighborhood covering reduction, abbreviated as NCR-EBRB, for diabetes prediction and diagnosis. During the model construction phase, the Extreme Gradient Boosting (XGBoost) method is first employed for feature importance evaluation to reasonably screen key features and effectively reduce model dimensionality. In the model inference phase, the Neighborhood Covering Reduction (NCR) method is adopted to implement rule reduction in the rule base, combined with a threshold-based rule activation strategy to filter out inefficient rules, ensuring efficient reasoning processes and effective result output. During the model optimization phase, the Projection Covariance Matrix Adaptive Evolution Strategy (P-CMA-ES) is applied to optimize the parameters of the streamlined rule base, aiming to identify optimal parameter configurations for further improving model performance. Through this meticulous parameter tuning, the diagnostic accuracy is enhanced, and the robustness of the model is improved.