## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Results and Analysis  
The effectiveness of a machine learning model is determined in terms of its uniformity and scalability. Various performance metrics are available to figure out the efficiency of any newly developed predictive model. In this subsection, our proposed model using 'Attribute Evaluator' was evaluated on several chronic disease datasets and its performance has been assessed according to a series of vital performance parameters. The obtained Results are discussed in detail further on in this subsection. The proposed 'Attribute Evaluator' comprising K-Means clustering, the CFS method and the BFS method, was used to generate an optimized attribute subset. It was used as a dimension reduction approach on 15 disease datasets collected from the UCI data repository. It was further compared with the CFS filter method and the BFS wrapper method in selecting the reduced attribute set. The result is shown in the table. The difference in performance is quite evident, especially on datasets with a higher number of attributes. The proposed model (K-Means, CFS and BFS) performs very well in generating a reduced attribute set for almost all disease dataset. With a relatively lower attribute set, the proposed model performs marginally better. The Arrhythmia data samples initially contained 279 attributes, the proposed model removed outliers and redundancies, reducing the number of attributes to 262. However, when optimizing diabetes data which contained only 8 attributes, the distinction was not as evident, as the attribute set was reduced to 6. This set is identical to the reduced set using wrapper and filter approach taken alone. Hence, it can be inferred that the dimensionality reduction of the proposed 'Attribute Evaluator' is optimal and beneficial when the analysed disease data samples are large and have a relatively higher number of attributes. On the contrary, when smaller datasets are evaluated the model's performance is equivalent to the wrapper or filter approach. Table 13 gives a summary of the results. The proposed 'Attribute Evaluator' approach is combined with decision tree for the classification of datasets. Its performance is assessed according to performance indices, such as accuracy rate, specificity, sensitivity and F-Score. Figure 17 shows the evaluation result. It has been observed that classification with decision tree yielded a very impressive result and the performance was very consistent on almost all disease datasets. The classification of cervical cancer data generated the highest accuracy of 98.4% while the lowest accuracy was 94.8% with the Parkinson disease dataset. A maximum specificity value of 98.2% was achieved on cervical cancer data and a minimum specificity value of 93.6% was achieved on autism disorder dataset. Similarly, a very high sensitivity rate of 97.6% was achieved on thyroid data and the lowest sensitivity of 92.9% on autism data. Harmonic mean of specificity and sensitivity called the F-Score metric was also evaluated. Both thyroid and cervical cancer recorded the highest F-Score value of 97.8% while autism gave a comparatively low value of 93%. In general, the generated mean values for accuracy, specificity, sensitivity and f-score were 96.8%, 96.5%, 95.6% and 96.2%, respectively using the proposed attribute evaluator approach.