## 3 Proposed Methodology
### Feature Selection  
A heat map represents the Pearson correlation coefficient matrix. The linear correlation between two properties can be described by the Pearson correlation coefficient. Figure 4 depicts the heat map.  
We observe that several of the features we developed have a stronger linear link with one another. The Pearson correlation coefficient between these two features is close to one, which supports the heat map's message. Two features that are completely correlated with one another can be expressed by one another, which means that the information these two features carry is identical. The accuracy of the model can be increased while causing less data information loss for the two features with a high linear association by deleting one of them. Figure 4 shows that the absolute values of the heat map of attribute correlations to the class label demonstrate that age, specific gravity, and blood pressures have the highest correlations (more than 0.5). The secondary characteristics of sugar and albumin, for example, have correlations greater than 0.2.  
Figure 4: Correlation heat map.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{2}{c}{**Before SMOTE**} & **After Smote** \\ \cline{2-3}  & **Overall (N = 400)** & **Overall (N = 750)** & **(Mean \(\pm\) SD)** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Age \\ (Mean \(\pm\) SD) \\ \end{tabular} } & \multirow{2}{*}{\(38.52\pm 16.49\)} & \multirow{2}{*}{\(38.40\pm 16.53\)} & \multirow{2}{*}{-} \\ \cline{1-1}  & Male & & & & \\ \cline{1-1}  & Female & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} CKD \\ \end{tabular} } & \multirow{2}{*}{\(250\) (62.5)} & \multirow{2}{*}{\(300\) (40)} & \multirow{2}{*}{\(38.25\pm 16.55\)} \\ \cline{1-1}  & Non-CKD & & & & \\ \hline \multicolumn{1}{c}{Testing Samples} & \multicolumn{2}{c}{-} & \multicolumn{2}{c}{150 (20)} & \multirow{2}{*}{\(38.29\pm 16.49\)} \\ \hline \hline \end{tabular}
\end{table}
Table 3: SMOTE analysis.  
The RFE approach is selected to choose the most prominent features by identifying those that have a high correlation with the target. DT is used as a base model for the RFE approach for feature selection. From the 24 dependent attributes included in the dataset, we chose 14 relevant features apart from class label to develop the prediction model. Table 4 provides the attributes that were chosen. During preprocessing, the missing values are replaced with the mean value for a few attributes, such as age, and the highest threshold was taken for a few attributes, including BP.  
The nominal error rate, as determined by [PERSON]'s MCAR test on a sample of 400 records, is shown in Figure 5. Incorporating incomplete data flow factors into the inference approach is useless if they do not correlate with the result. It is a fallacious perception to integrate any attributes in the inference process only because there is a possibility that they are associated with multicollinearity in the result.  
\begin{table}
\begin{tabular}{c c} \hline
**Attributes/Features** & **Values Used** \\ \hline Age (A) & Discrete integer values \\ \hline Blood pressure (BP) & Discrete integer values \\ \hline Specific gravity (sg) & Nominal values \\ \hline Albumin (Al) & Nominal values \\ \hline Sugar(su) & Nominal values \\ \hline Blood glucose random (bgr) & Numerical value \\ \hline Blood urea (bu) & Numerical value \\ \hline Serum creatinine (sc) & Numerical value \\ \hline Sodium (sod) & Numerical value \\ \hline Potassium (pot) & Numerical value \\ \hline Haemoglobin (haem) & Numerical value \\ \hline Packed cell volume & Numerical value \\ \hline White blood cell count & Numerical value \\ \hline Red blood cell count & Numerical value \\ \hline \end{tabular}
\end{table}
Table 4: Attributes/features chosen.  
Figure 5: [PERSON]’s MCAR test on 16 attributes.