In the research of computer-aided diagnosis, the shortage of disease feature dimension curse and the imbalance of medical samples have always been the focus of research on diagnostic decision support systems. For these two problems, we propose a feature selection algorithm based on association rules and an integrated classification algorithm based on random equilibrium sampling. We extracted and cleaned the electronic medical record text obtained from the hospital to obtain a diabetes data set. The proposed algorithm was verified in this data set and the public data set UCI. Experimental results show that the feature selection algorithm based on association rules is better than the CART, ReliefF and RFE-SVM algorithms in terms of feature dimension and classification accuracy. The proposed integrated classification algorithm based on random equalization sampling is superior to the comparative SMOTE-Boost and SMOTE-RF algorithms in macro precision, macro-full rate and macro F1 value, which embodies the robustness of the algorithm.

在计算机辅助诊断研究中，疾病特征维数诅咒的不足和医学样本的不平衡一直是诊断决策支持系统研究的重点。针对这两个问题，提出了一种基于关联规则的特征选择算法和一种基于随机均衡抽样的综合分类算法。我们提取并清理了从医院获得的电子病历文本，以获得糖尿病数据集。在该数据集和公共数据集UCI中验证了所提出的算法。实验结果表明，基于关联规则的特征选择算法在特征维数和分类精度上均优于CART，ReliefF和RFE-SVM算法。