## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & & **Merits** & & **De-Merits** \\ \hline \multirow{4}{*}{Filter} & \(\bullet\) & & & Surrogate classification measures \\  & \(\bullet\) & Suitable for large datasets & & generally do not reflect the behavior of \\  & \(\bullet\) & Computationally less expensive & & the actual classifier \\  & \(\bullet\) & Produces more generalized results & & \(\bullet\) & Comparatively less accurate and \\  & & & & optimized than wrapper techniques \\  & & & & \(\bullet\) & Feature interaction is not taken \\  & & & & into account \\ \hline \multirow{4}{*}{Wrapper} & \(\bullet\) & Produces relatively accurate results & & \(\bullet\) & Computationally more expensive \\  & \(\bullet\) & Accounts for feature interaction & & \(\bullet\) & Tends to over-fit on small datasets \\  & \(\bullet\) & Produces optimized result with less & & \(\bullet\) & Lacks scalability \\  & & number of features & & & \\ \hline \multirow{4}{*}{Embedded} & \(\bullet\) & Fine-tuned for enhancing the & & \(\bullet\) & Tightly coupled with a specific \\  & & performance of a particular classifier & & classification model \\ \cline{1-1}  & \(\bullet\) & Relatively less expensive than & & \(\bullet\) & Lacks generalization capabilities for \\ \cline{1-1}  & & wrapper techniques & & other models \\ \cline{1-1}  & \(\bullet\) & Generally more robust in terms & & \(\bullet\) & Generally expensive than \\ \cline{1-1}  & & of overfitting & & filter methods \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison among different feature subset selection techniques. employed three models, i.e., K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy for decision modeling of the CKD patients. The reported approach achieved an accuracy of 93%. It is also reported that SVM was more robust in dealing with noisy data as compared to other models and hence achieved an accuracy of 99% [23]. [PERSON] [24] used the C4.5 decision tree model on the CKD dataset. The resultant tree model produced 8 production rules of the form IF <condition> THEN <conclusion> and achieved an accuracy of 98.25%, whereas, [PERSON] et al. [26] reported lower accuracy on the same dataset. Furthermore, the authors also identified 5 salient features in their study. In another study, the same framework as reported in [26] is used, in which they used three classifiers on a CKD dataset which was acquired from Prince Hamza Hospital, Jordan. The study reported that the decision tree model performed reasonably well on a number of performance metrics [27]. [PERSON] et al. [27] used several classification models such as SVM, Naive Bayes, KNN, and decision tree on the CKD dataset. Subsequently, a feature ranking is generated from which the top 10 features were selected. It is reported that the decision tree algorithm produced a model yielding accuracy of 99.75%. [PERSON] et al. [18] proposed a feature selection technique for SVM based classification model. The authors used a hybrid feature selection by leveraging both filter and wrapper methods. They reported an accuracy rate of 98.50% on SVM using 'Best First' search technique using 11 attributes. Likewise, [PERSON] and [PERSON] [8] selected the top 13 features for feature selection based on an ensemble of feature selection techniques. Afterward, the authors performed classification using an optimized RF classifier for the CKD dataset. The reported accuracy of 100% was over the reduced CKD dataset. In Ref. [22], the authors experimented with SVM and Artificial Neural Network (ANN) on the CKD dataset. They reported that ANN produced a comparatively higher accuracy model as that of SVM. All the experiments are performed on the top 12 features. Apart from feature selection, the data discretization process is reported to have a favorable effect on the decision tree-based model construction [28]. [PERSON] et al. [15] experimented with several different data imputation configurations on a set of multiple classifiers.