## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets  
The objective of this study is to determine the impact of the filter and wrapper approach for attribute selection on the classification performance. In this case, the focus was on the classification of disease datasets. This section presents popular chronic disease datasets used in research. System configuration requirements are provided in this section. The implemented computational techniques are presented along with the software packages. Graphs illustrating the impact of filters and wrappers on classification performance are presented. The results obtained upon the implementation of the proposed graphical data flow model are discussed. Chronic diseases are a critical worldwide public health challenge [76]. Diabetes, hepatitis, liver disorders, lung cancer, breast cancer and heart disease are the most common chronic diseases [77]. Therefore, there is a significant number of patients suffering from these chronic diseases. Consequently, it is crucial to have a faster mechanism for effective patient support. In this study, three chronic disease datasets are used. These include diabetes, breast cancer and heart disease datasets. These are the most widely observed chronic diseases [78]. The datasets have been collected, extracted and aggregated from the UCI repository, the University of California. The PIMA Indian diabetes dataset utilized in our research constitutes a total of 8 distinct attributes along with 768 instances [79], as shown in Table 3; Table 4 depicts the breast cancer data used in this study. There was a total of 10 distinct attributes with 286 unique records [79]. Figure 4: Distribution graph of the Filter and Wrapper approach in our literature survey. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Attribute** & **Description** & **Domain Range** \\ \hline preg & Frequency of Pregnancy & 0–15 \\ plas & Concentration of Plasma glucose level & 0–199 \\ pres & Diastolic blood pressure & 0–122 (mm Hg) \\ skin & The thickness of Triceps skin (mm) & 0–99 (mm) \\ insu & Serum insulin (2-h) & 0–846 (mu U/mL) \\ mass & Body mass index & 0–67.1 (kg/m\({}^{2}\)) \\ Pedi & Diabetes pedigree function & 0.08–2.42 \\ age & Age of person & 21–81 years \\ class & Class label & 0 = absence; 1 = presence \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PIMA Indian dataset details [79]. The heart disease dataset, as shown in Table 5, is implemented in this analysis. It consists of 270 instances with two different class labels to detect the presence and absence of heart disease in patients [79]. The data record contains 13 unique symptom attributes. Our experiment has been conducted with 10-fold cross-validation using the attribute selection function of the Waikato Environment for Knowledge Analysis (WEKA) tool [80]. This software tool supports distinct functionalities like data visualization, data pre-processing, data classification, cluster analysis and pattern mining. The dataset which has been input in the WEKA software is in Attribute-Relation File Format (.arff) format [81; 82]. A 64-bit Windows Operating system with a Quad-core processor and a minimum of 8 GB RAM is a key requirement. Moreover, Java 1.7 version or higher is required to install WEKA software. Several algorithms and methods have been used in this analysis. The algorithms used in the study have been made available in the WEKA software in the form of different packages and libraries. These packages are shown in Table 6 to give a brief introduction to the computational techniques and methods used in the study. Table 7 highlights the important packages of the WEKA software used in this analysis.