<p>The application of machine learning algorithms in clinical disease diagnosis has significantly improved the accuracy and efficiency of clinical predictions. However, many clinical datasets suffer from a severe class imbalance, which causes the predictions to be biased towards the majority class. Traditional sampling methods often alter the original distribution of samples, which negatively impacts the prediction accuracy and generalization ability of models. In this paper, we propose a cascaded proportion-balanced ensemble learning model for prediction of disease activity in rheumatoid arthritis, which consists of a symmetric three-part structure, with each part being a cascaded structure made up of a base classifier and a sampling model. Through cascaded proportion sampling, the majority class samples are randomly deleted and minority class samples are replicated in proportion, gradually balancing the training sample distribution for the base classifiers. Then, the sample sizes of each class are adjusted in reverse to achieve overall sample balance for the cascaded structure. We use an asymmetric Beta distribution function to fit the weight changes of base classifiers, controlling the curve shape with two variables to dynamically adjust the weight distribution of each base model, ensuring the symmetry and effectiveness of the weighted ensemble model. The Bayesian optimization algorithm is employed to dynamically search for the optimal hyperparameters of the classification model, further enhancing prediction accuracy and generalization ability. We evaluate the model using 5-fold cross-validation on a dataset of 10 514 rheumatoid arthritis samples from the Guang’anmen Hospital, China Academy of Chinese Medical Sciences. The evaluation metrics show GMarea of 0.60, Precision of 0.78, and AUC of 0.83. Compared to various typical models, the proposed model demonstrates better prediction accuracy and generalization performance.</p>