Medical data usually have missing values; hence, imputation methods have become an important issue. In previous studies, many imputation methods based on variable data had a multivariate normal distribution, such as expectation-maximization and regression-based imputation. These assumptions may lead to deviations in the results, which sometimes create a bottleneck. In addition, directly deleting instances with missing values may have several problems, such as losing important data, producing invalid research samples, and leading to research deviations. Therefore, this study proposed a safe-region imputation method for handling medical data with missing values; we also built a medical prediction model and compared the removed missing values with imputation methods in terms of the generated rules, accuracy, and AUC. First, this study used the kNN imputation, multiple imputation, and the proposed imputation to impute the missing data and then applied four attribute selection methods to select the important attributes. Then, we used the decision tree (C4.5), random forest, REP tree, and LMT classifier to generate the rules, accuracy, and AUC for comparison. Because there were four datasets with imbalanced classes (asymmetric classes), the AUC was an important criterion. In the experiment, we collected four open medical datasets from UCI and one international stroke trial dataset. The results show that the proposed safe-region imputation is better than the listing imputation methods and after imputing offers better results than directly deleting instances with missing values in the number of rules, accuracy, and AUC. These results will provide a reference for medical stakeholders.
Optimization Based Undersampling for Imbalanced Classes
