## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Comparison of Filter Approach  
This subsection highlights the importance of the filter approach for chronic disease datasets. The filter approach was used to generate a reduced attribute set for different chronic disease datasets, where the attributes were the relevant symptoms. 270 heart disease dataset instances have been collected with 13 distinct attributes initially present. These samples were subjected to the filter approach. Individual attribute score can be seen from the attribute significance graph and as seen 'ca' and 'Exang' are the least relevant attributes according to the filter approach. Figure 6 highlights the attribute significance analysis concerning heart disease data. Figure 7 depicts the attribute significance graph of the Diabetes dataset that has a total 768 instances with 8 attributes. The attribute'skin' generated a comparatively lower score of 0.3 with filter approach. Similarly, the filter approach was evaluated on a breast cancer dataset. The attribute significance graph is presented below. The significance score of the attribute 'irradiation' was the lowest, with only 0.3, in comparison to other attributes. It is highlighted in Figure 8.  
Figure 6: Attribute Significance Graph for the Heart disease dataset with Filters.  
An attribute significance analysis was performed on all three datasets based on ranking of attributes. The low ranked attributes are given less priority. The set of relevant attributes obtained by the CFS method from the heart disease dataset was \(11\) while in the IG method and the chi-square method it has been reduced to 10 attributes. When filter methods were applied to diabetes data, it was seen that both the CFS and the IG methods included 7 attributes in the optimized attribute set on the basis of a priority ranking. The chi-square method included 6 attributes in the resulting set. It is observed that the CFS and chi-square methods included 7 attributes in the reduced sets of attributes  
Figure 8: Attribute Significance Graph for the Breast Cancer dataset with Filters.  
Figure 7: Attribute Significance Graph for the Diabetes dataset with Filters.  
while the IG filter method of evaluation included 8 attributes in the resulting attribute set. When filter methods were applied to the breast cancer dataset, it was found that 2 attributes had less importance for both the CFS and Chi-square methods, while only 1 attribute was irrelevant for the IG method. Table 8 shows an overall analysis of the impact of filter methods on disease datasets and the sets of selected attributes.  
After determining the relevant score level of all attributes of the chronic disease datasets, the reduced and optimized attribute set is classified using the Decision Tree algorithm. The classification process has been conducted to detect the presence or absence of the chronic disorder in patients. The filter methods have been applied to classify the chronic disease datasets using the Decision Tree classifier; the obtained results are interesting. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. In this work, accuracy rate is selected as the heuristic function to rank attributes. CFS method performed better than others as it evaluates the attribute subsets which exhibit strong association with class label but least dependence on each other. Thus, weekly correlated attributes are less significant and thus dropped. With heart disease dataset, the CFS method achieved an accuracy of 93.8%. Furthermore, this method produces an 89.5% accuracy rate and only a 1.02 s delay in execution with the diabetes dataset. The CFS method also provided 96.8% accuracy and a minimum execution time delay of only 1.01 s when classified with breast cancer data. Table 9 presents a tabular view of classification accuracy and latency analysis using filter methods.