# The Feature Selection Effect on Missing Value Imputation of Medical Datasets
## 5 Conclusions  
Missing value imputation is a solution for the incomplete dataset problem. Given that the imputation process requires a set of observed data for imputation modeling, regardless of whether statistical or machine learning techniques are used to produce estimations to replace the missing values, the quality of the observed data is critical. In this paper, we focus on the problem from the feature selection perspective, assuming that some of the collected features may be unrepresentative and affect the imputation results, leading in turn to degradation of the final performance of the classifiers when compared with the ones where feature selection is performed. For the experiments, five different medical domain datasets containing various numbers of feature dimensions are used. In addition, three different types of feature selection methods are compared, namely information gain (IG) as the filter method, genetic algorithm (GA) as the wrapper method, and decision tree (DT) as the embedded method. For missing value imputation, the multilayer perceptron (MLP) neural network, k-nearest neighbor (KNN), and support vector machine (SVM) models are constructed individually. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Imputation Methods**} \\ \cline{2-5}  & _GA+MLP_ & _IG+MLP_ & _MLP_ & \\ Arrhythmia (38.27) & 29.32 & 29.13 & 29.13 & 29.13 \\ Breast Cancer (85.17) & 82.31 & 81.68 & 82.29 & 82.39 \\ \cline{2-5}  & _DT+KNN_ & _GA+KNN_ & _IG+KNN_ & _KNN_ \\ Arrhythmia (38.27) & 29.32 & 29.13 & 29.13 & 29.13 \\ Breast Cancer (85.17) & 82.95 & 83.50 & 82.29 & 82.39 \\ \cline{2-5}  & _DT+SVM_ & _GA+SVM_ & _IG+SVM_ & _SVM_ \\ Arrhythmia (38.27) & 29.32 & 29.13 & 29.13 & 29.13 \\ Breast Cancer (85.17) & 82.82 & 83.34 & 82.29 & 82.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results for the different methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Original features** & **DT** & **GA** & **IG** \\ \hline Arrhythmia & 279 & 30 & 70 & 223 \\ Breast Cancer & \(117\) & 46 & 47 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The numbers of selected features for DT, GA, and IG. The experimental results show that the combination of feature selection and imputation can make the classifier (i.e., SVM) perform better than the baseline classifier without feature selection for many datasets with different missing rates. For lower dimensional datasets, using GA and IG for feature selection is recommended, whereas DT is a better choice for higher dimensional datasets. Some issues should be considered in future research work. First, other missingness mechanisms, including MAR and MNAR, can be investigated for the feature selection effect. In addition, some datasets that naturally have specific numbers of missing data (i.e., specific missing rates) can be used. On the other hand, some other differences among the datasets that could influence the results can also be used, for example binary or multiple differences, or even the difficulty in classification where the datasets contain much higher dimensions or larger numbers of instances and classes. Second, in performing feature selection and missing value imputation, the major limitation is that a number of observed data (i.e., _D_complete) must be provided for the feature selection methods to select some representative features and imputation models to produce estimations to replace the missing values. Therefore, the effect of using different numbers of observed data on the feature selection and imputation results should be investigated. On the other hand, for datasets that do not contain a sufficient number of complete data samples, the over-sampling techniques [46; 47] used to create synthetic samples can be employed.