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Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation–maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes, this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly removing instances with missing values has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations. Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]