## 5 Conclusions and Future Scope  
This paper has presented a comparative analysis of wrapper and filter selection while determining its impact on prediction performance in different chronic disease datasets. The Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. The overall research analysis can be bifurcated into two parts. In the first part, a comparative analysis of wrappers and filters has been carried out and its impact on three chronic disease datasets was analysed. Heart disease, diabetes and breast cancer data were contained in the datasets. CFS, IG and Chi-Square were the filter methods used while BFS, LFS and GSS were the wrapper methods used in the research. An optimum accuracy of 93.8%, 89.5% and 96.8% was recorded for heart disease, diabetes and breast cancer, respectively, using the CFS method. Latency delays of 1.08 s, 1.02 s and 1.01 s were observed  
Figure 22: Comparison of error rate between the Proposed Hybrid Decision Tree and the classical decision tree method on chronic disease datasets.  
using the same method on the respective datasets. Among wrapper methods, BPS gave the best classification performance. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved for heart disease, diabetes and breast cancer, respectively. Latency delays of 1.42 s, 1.44 s and 132 s were observed using the same method on the respective datasets. Moreover, it was noted that though wrappers generated maximum classification accuracy, they were compute-intensive. Classifications with filter methods incurred less computational cost and their execution time was lower than that of wrapper methods. It was inferred that many attribute selection methods did not give much importance to the structure of attributes and the possible outliers were ignored during the generation of an optimized attribute set.  
In the second part of the paper, an integrated hybrid attribute evaluation method has been proposed. It combined a new variant of K-Means cluster analysis named Integrated Supervised K-Means along with the CFS and BFS methods. This new variant of k-means algorithm used regression approach to generate cluster member probabilities which is used to detect less relevant instances from participating in finding cluster mean. Classification with the proposed method using an improved decision tree maximise the performance. The improve decision tree used clustering approach which classifies majority instances. The remaining instances were input to the decision tree unit which classifies them into respective classes. It was evaluated on 14 different chronic disease datasets using various performance metrics. Optimum mean values of 96.8%, 96.5%, 95.6% and 96.2% were recorded for accuracy, specificity, sensitivity and f-score. The use of the newly proposed integrated attribute evaluator model along with improve decision tree classifier generated a mean accuracy of 96.85 which is more than 96.1% accuracy rate generated by using decision tree alone. The mean error rate recorded with the hybrid classification model is as low as 0.642. Thus, it can be suggested for chronic disease assessment which can assist medical experts, acting as a decision support system. Furthermore, this analysis can support future research initiatives using machine learning for chronic disease classification. Future lines of research involve the application of the proposed model to image related datasets. Moreover, assessing impact of ensemble learning and image datasets on attribute evaluators will be an important element of future research.  
All authors have designed the study, developed the methodology, performed the analysis, and written the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.