## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Results and Analysis
A mean accuracy rate of 96.7% was recorded with the proposed approach while a slightly less accuracy of 94.5% was observed with MRMR method as observed in Figure 20.  
The data from the datasets is partitioned before being used, using a 10% of the data for adjusting the parameter, and the rest for a 10-fold cross-validation [90], which implies that each fold uses 81% of the total data of the data set for training and 9% for testing. The chronic disease datasets under use is  
Figure 20: Comparative Study of the Classification Accuracy of the Proposed Attribute Evaluator Method and MRMR method. divided before being implemented. Among the data samples, 15% instances were utilized to adjust and generate best parameters. Remaining 85% data were used for cross-validation where 75% data samples were used as training set while the rest 10% instances were used for testing. Classification accuracy analysis was done between the newly developed hybrid classifier model and decision tree alone. A maximum 98.4% accuracy was generated using cervical cancer dataset while the mean accuracy recorded was 96.8% with the hybrid classification model [91; 92]. Decision tree too gave a good accuracy performance but slightly less than the hybrid model as seen in Figure 21.  
The performance of the proposed hybrid clustering based decision tree classification model was compared with the general decision tree classifier with respect to the model prone to error in Figure 22. It was observed that a least error rate generated was 0.116 with diabetes dataset when used with the new hybrid classification model. An error rate of 1.84 was noted with breast cancer data samples. Similarly, the hybrid decision tree model generated less error rate when compared to the decision tree classifier taken alone. The mean error rate recorded with the new improved hybrid model was just 0.642 as compared to 1.73 with decision tree classifier. Figure 18.0 Comparison of accuracy rate between the Proposed Hybrid Decision Tree and the classical decision tree method on chronic disease datasets. Thus, in this section, a new integrated attribute evaluator approach using an enhanced K-Means clustering, the CFS filter method and the BFS wrapper method has been presented and evaluated on 14 different chronic disease datasets. The impact of the proposed model was clearly visible on datasets with higher numbers of attributes. As the number of attributes increased, the relevance of attributes was more evident, and outliers were eliminated. Various performance indicators have been used to determine its classification performance. Impressive maximum metric values were recorded. 96.7%, 96.5%, 95.6% and 96.2% were the mean percentages for accuracy, specificity, sensitivity and f-score, respectively. Performance of improved decision tree model was also compared with decision tree classifier taken alone and it was noted that the improved decision tree performed much better in terms of accuracy rate and error rate. It is due to the fact that clustering approach in improved decision tree classifies majority of data samples. Only the left-over unclassified instances were fed into decision tree unit for further classification. Hence it is more effective and less prone to error.