# The Feature Selection Effect on Missing Value Imputation of Medical Datasets  
[PERSON]  
1 Department of Nursing, Dittmann Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan; [EMAIL_ADDRESS]  
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3 Department of Information Management, National Central University, Taoyuan 320, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Information Management, National Central University, Taoyuan 320, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
4 School of Medicine, China Medical University, Taichung 404, Taiwan; 05 Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi 600, Taiwan; 55 Correspondence:[EMAIL_ADDRESS]; Tel. : +88652359630  
the simplest solution is based on case deletion (or listwise deletion), in which data containing missing values are deleted. However, it is problematic when missing data are not random or the missing rate for the whole dataset is larger than a certain value, for example 10% [11, 13]. Model-based imputation methods using machine learning techniques have been shown to outperform many other statistical techniques [14, 15, 16, 17, 18, 19]. In general, these types of model-based imputation methods are based on machine learning techniques and involve training using a set of complete data to produce estimations to replace the missing values in an incomplete dataset. However, since a collected (incomplete) dataset must contain a number of features (i.e., input variables) to represent the data, it is likely that some of the features will not be representative, which can affect the discriminatory power of the data mining algorithms. In other words, redundant and irrelevant features or unwanted features from the collected dataset must be filtered out; otherwise the mining performance will be affected. This situation could be even worse when ultra-high or hyperdimensional datasets containing a very large number of features are used, which is called the curse of dimensionality [20]. For the purpose of missing value imputation, performing feature selection over the observed data to filter out unrepresentative features could make the imputation process more efficient, since some of the missing features, which may be regarded as unrepresentative, are not required for imputation. Moreover, feature selection is able to make the imputation model trained by the lower dimensional observed data provide better estimations for the rest of the missing features. In literature, several studies have focused on this issue [21, 22]. However, since feature selection methods can be classified into filter, wrapper, and embedded methods [23], none of them consider all three types of methods for missing value imputation, especially for medical datasets. Additionally, the numbers of features in their chosen datasets are also very small (i.e., 13 to 105 and 6 to 9). Therefore, the research objective in this study is to examine the effects of performing three types of feature selection methods on model-based missing value imputation over different medical domain datasets. For feature selection, three different types of feature selection methods are employed; information gain (IG) as the filter-based method, genetic algorithm (GA) as the wrapper-based method, and decision tree (DT) as the embedded-based method. In addition, three popular machine learning techniques are used for the imputation process, namely the k-nearest neighbor (k-NN), multilayer perceptron (MLP), and support vector machine (SVM) approaches. The contribution of this paper is two-fold. First, the effect of performing feature selection on missing value imputation is examined for various domain problems. Second, the best combinations of feature selection and imputation methods are identified for datasets with different dimensionality scales. The rest of this paper is organized as follows. A review of the related literature is given in Section 2, including the types of missing values and the missing value imputation process.