## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Chronic Disease Dataset** & **Instances** & **Initial Attribute Set** & **K-Means** & **CFS** & **BFS** & **CFS + BFS** &
\begin{tabular}{c} **Proposed Attribute** \\ **Evaluator** \\ \end{tabular} \\ \hline Pima Indians Diabetes & 768 & 8 & 8 & 7 & 7 & 6 & 6 \\ Chronic Kidney Disease & 400 & 25 & 23 & 22 & 22 & 21 & 20 \\ Statlog (Heart) & 270 & 13 & 12 & 11 & 10 & 9 & 9 \\ Breast Cancer Wisconsin & 699 & 10 & 10 & 9 & 9 & 8 & 8 \\ Arrhythmia & 452 & 279 & 274 & 271 & 268 & 264 & 262 \\ Hepatitis & 155 & 19 & 19 & 18 & 17 & 16 & 15 \\ Lung Cancer & 32 & 56 & 54 & 53 & 51 & 50 & 49 \\ Parkinson’s & 197 & 23 & 22 & 21 & 20 & 19 & 18 \\ Liver Disorder & 345 & 7 & 7 & 7 & 6 & 6 & 6 \\ Primary Tumour & 339 & 17 & 17 & 16 & 15 & 14 & 13 \\ Thyroid Disease & 7200 & 21 & 20 & 18 & 17 & 16 & 16 \\ Cervical Cancer & 858 & 36 & 35 & 34 & 33 & 32 & 30 \\ Breast Cancer & 286 & 9 & 9 & 8 & 8 & 8 & 8 \\ Autistic Disorder & 292 & 21 & 20 & 20 & 19 & 19 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The Impact of Proposed Attribute Evaluator Method on disease datasets. The classification accuracy of the decision tree with proposed attribute optimization model was evaluated with some popular filter approaches to determine the preciseness of the generated attribute set. The result is illustrated in Figure 18. CFS, IG and Chi-square methods were used as filter methods. Highly consistent performance was observed with the proposed model on almost all disease datasets with the exception of lung cancer and Parkinson disease. While the accuracy of the CFS filter method for lung cancer and Parkinson was noted to be 95.7% and 95.2%, slightly lower values of 95.6% and 94.8% were obtained using the proposed model. Overall, the proposed classification model outperformed other filter approaches. A comparative analysis of the accuracy achieved by the proposed model and the wrapper approach has also been carried out, using decision tree as classifier. The wrapper methods considered in the comparison were: BFS, LFS and GSS. The result is analysed in Figure 19. A highly optimal classification performance was recorded. Except the classification of breast cancer data, other disease datasets generated a very high accuracy rate has been achieved by compared to its wrapper approaches. Only in breast cancer data, the BFS method 96.8% of accuracy while the proposed model slightly lagged behind with 96.2%. Overall, the use of the proposed model for classification has yielded optimal performance; superior to that of wrappers. Figure 17: Evaluation of the Classification Performance of the Proposed Attribute Evaluator Method. Figure 19: Comparative Study of the Classification Accuracy of the Proposed Attribute Evaluator Method and Other Wrapper Methods. Figure 18: Comparative Study of the Classification Accuracy of the Proposed Attribute Evaluator Method and Other Filter Methods. The proposed attribute evaluator approach was further compared with the Minimum Redundancy Maximum Relevance (MRMR) method which is a widely popular attribute selection technique being applied to several datasets in different domains [89]. It is used as a heuristic method and is applicable for both discrete as well as continuous data samples. It computes the most promising attributes by measuring the relevance and redundant attributes. The MRMR method was evaluated against the proposed attribute evaluator using decision tree as classifier. 14 chronic disease datasets as mentioned in Table 13 were used for analysis. MRMR method gave a very good performance but the proposed attribute evaluation approach generated a better outcome with almost all datasets.