## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Comparison of the Wrapper Approach  
The result of the application of the wrapper approach to the chronic disease datasets is summarized in this section. The attribute significance graph was developed for all three chronic datasets. The evaluation of wrapper methods was carried out on the heart disease data instances. The selected attribute list is shown in Figure 9 in the form of an attribute significance graph. In total 3 attributes \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Chronic Disease Dataset** & **Dataset Details** & **CFS** & **IG** & **Chi-Square** \\ \hline \multirow{3}{*}{Heart Disease Dataset} & Number of instances & 270 & 270 & 270 \\  & Initial Attribute set & 13 & 13 & 13 \\  & Reduced Attribute set & 11 & 10 & 10 \\ \multirow{3}{*}{Diabetes Dataset} & Number of instances & 768 & 768 & 768 \\  & Initial Attribute set & 8 & 8 & 8 \\  & Reduced Attribute set & 7 & 7 & 6 \\ \multirow{3}{*}{Breast Cancer Dataset} & Number of instances & 286 & 286 & 286 \\  & Initial Attribute set & 9 & 9 & 9 \\ \cline{1-1}  & Reduced Attribute set & 7 & 8 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Impact of filter methods on disease datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chronic Disease Dataset** & **Filter Method** & **Search Method** & **Classifier** & **Accuracy** & **Latency** \\ \hline \multirow{3}{*}{Heart Disease Dataset} & CFS & Ranker & Decision tree & 93.8\% & 1.08 s \\  & IG & Ranker & Decision tree & 89.6\% & 1.34 s \\  & Chi-square & Ranker & Decision tree & 91.2\% & 1.16 s \\ \multirow{3}{*}{Diabetes Dataset} & CFS & Ranker & Decision tree & 89.5\% & 1.02 s \\  & IG & Ranker & Decision tree & 88.2\% & 1.14 s \\ \multirow{3}{*}{Breast Cancer Dataset} & Chi-square & Ranker & Decision tree & 88.8\% & 1.05 s \\  & CFS & Ranker & Decision tree & 96.8\% & 1.01 s \\ \multirow{3}{*}{Breast Cancer Dataset} & IG & Ranker & Decision tree & 89.2\% & 1.09 s \\  & Chi-square & Ranker & Decision tree & 86.8\% & 1.21 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Analysis of classification accuracy and latency with filter methods on disease datasets. which include 'Ca', 'Exang', 'Oldpeak' were found to be least relevant. The attribute'skin' was recorded with a significance score of 0.3 in the diabetes dataset and hence eliminated. Figure 10 shows an attribute significance graph for diabetes data. The wrapper approach was evaluated on the breast cancer dataset and its attribute significance graph is depicted in Figure 11. Every method achieved a score level of around 0.3, 'irradiation' has been found to be the least relevant attribute. It can be clearly seen in the graphical analysis that three attributes were relatively less relevant than the others in case of the heart disease dataset. In the diabetes dataset, only one attribute was less important with both diabetes and breast cancer dataset samples. Figure 9: Attribute Significance Graph for the Heart disease dataset with Wrappers. Figure 11: Attribute Significance Graph for the Breast cancer dataset with Wrappers. Figure 10: Attribute Significance Graph for the Diabetes dataset with Wrappers. The reduced sets of attributes obtained by the BFS, LFS and GSS methods were identical, which constitutes 10 attributes while eliminating 03 irrelevant attributes. Wrapper methods were evaluated with the diabetes dataset to get an optimized attribute set. It has been observed that among eight attributes, only one attribute was identified as irrelevant. Consequently, it was eliminated from the resulting set in all three wrapper methods. The BFS and LFS methods obtained reduced sets of attributes for breast cancer data that contained eight attributes in total. The GSS method included only seven attributes in its reduced set. Table 10 depicts the tabular analysis of the reduced attribute sets after using the wrapper approach. The reduced attribute set is further classified using the decision tree algorithm.