## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Comparison of the Wrapper Approach
In heart disease prediction, it was observed that the best first search method generates an optimal accuracy rate of 94.7%. The greedy step wise search achieves a 94.2% accuracy and linear forward selection method lies in between these two methods with an accuracy of 92.6%. When the wrapper approach was applied to diabetes datasets, an accuracy of 95.6% was achieved with the greedy step wise search and the overall time delay was 1.97 s. Linear forward selection search had an accuracy of 90.6% with a latency delay of 1.56 s. The best first search method was relatively faster than the linear forward selection search method with only a 1.56 s execution delay, thereby achieving a 90.6% accuracy rate. In case of breast cancer classification, the greedy step wise method shows an impressive accuracy rate of 96.8% while the best first search method produces a lower accuracy of 92.8%. The latency delay with the greedy step wise search is also much lower, with 1.08 s, in comparison to the other two wrapper methods. In general, it was noted that BFS method offered better result than others. Reason for this good performance by BFS method was due to the fact that it is able to switch among different paths to solution with ease. It is able to find a good solution without figuring out all nodes and not being trapped at dead ends. Moreover, the time complexity of BFS method is quite less compared to others. The results are summarized in Table 11.  
The experimental evaluation was successfully conducted on three chronic disease datasets. Both filter and wrapper methods for attribute selection were implemented in the analysis. The results \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Chronic Disease Dataset** & **Dataset Details** & **BFS** & **LFS** & **GSS** \\ \hline \multirow{4}{*}{Heart Disease Dataset} & Number of instances & 270 & 270 & 270 \\  & Initial Attribute set & 13 & 13 & 13 \\  & Reduced Attribute set & 10 & 10 & 10 \\  & Number of instances & 768 & 768 & 768 \\  & Initial Attribute set & 8 & 8 & 8 \\  & Reduced Attribute set & 7 & 7 & 7 \\  & Number of instances & 286 & 286 & 286 \\ Breast Cancer Dataset & Initial Attribute set & 9 & 9 & 9 \\  & Reduced Attribute set & 8 & 8 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The impact of the wrapper methods on disease datasets. show that in wrapper approach, interaction with the classifier is high. However, it is more computationally intensive. The filter approach is independent of the classification algorithm. Consequently, it requires less computational cost. Furthermore, the classification accuracy rate of the wrapper approach was better than that of the filter approach. However, the latency rate is minimal with the filter approach. Table 10 summarizes the differentiating elements between both wrapper and filter approaches for attribute selection. It is worth noting that CFS method emerged as the best filter method while BFS clearly outperformed other wrapper methods. A Hybrid Classification Model Using 3-Step Attribute Evaluator and Improved Decision Tree to Enhance Classification Performance of Chronic Disease Datasets  
Today, the clinical sector faces many obstacles in analyzing heaps of the medical data of different patients. The detailed processing of such medical records is a big challenge due to the presence of unstructured and heterogeneous data in the records. There is an urgent need for the evolution of the clinical industry so as to address the issue of data processing and management. Identifying an appropriate attribute set from raw and unstructured data records is the main challenge. Any good machine learning model depends on the attributes that it is fed as input to the system. If the selected attribute set is more relevant, then the subsequent classification and prediction task will also be more efficient and accurate. Identifying the characteristics of attributes to discover their relevance in a dataset is a very critical issue. The majority of existing attribute selection methods overlook the structure of attributes.