Sample imbalance disease classification model based on association rule feature selection

Abstract

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.