## 6 Conclusions  
Cost-sensitive feature selection is one of the important areas of research where the cost of data acquisition plays an important role in the applicability of the solution. Generally, it is assumed that cost of data acquisition is the same, i.e., not necessarily zero. This assumption does not hold in certain application domains such as disease diagnosis. In this study, we used a well-known benchmarked dataset for disease diagnosis i.e., chronic kidney disease. A significant scholarly work is reported in designing algorithms and systems for chronic kidney disease efficiently. The proposed techniques for cost-sensitive feature selection reported in this study is in the continuation of CKD research, where the key objective is to enhance the performance of decision tree-based classification models. Decision tree-based classification models shown a great promise in the domain of medical diagnosis, especially in dealing with structured heterogeneous datasets, e.g., electronic medical records for chronic kidney disease patients. This research deals with addressing the applicability concerns of decision tree models through ensemble feature ranking techniques. In the proposed techniques a set of multiple feature scoring functions are used which are based on the decision tree family. It is also demonstrated that the partial solutions obtained from these scoring functions are not redundant and hence are useful in creating an ensemble technique. Furthermore, a heuristic technique based on feature weightage and the accumulated cost is introduced to select a subset of features. It is also demonstrated that the features selected based on the threshold value are both useful and cost-effective. Figure 11: Combined ranks of different approaches across performance and cost factors. The two proposed approaches for cost-sensitive ensemble feature selection primarily differ in the application of the threshold operation. Ensemble-1 combines multiple feature scores into a consolidated score and thereafter applies the threshold operation. In the case of ensemble-2, the threshold is applied to the individual lists obtained from multiple scoring functions. In this case, multiple feature subsets are produced as partial solutions. Afterward, all the solutions are combined using the majority voting scheme. Extensive experimentation is performed, in which it is demonstrated that although ensemble-2 is better in terms of general evaluation criteria for the CKD problem, ensemble-1 produces more consistent results. Both the aforementioned techniques are compared with other similar feature selection methods. It is demonstrated that cost-free feature selection techniques generally produce a solution with high accuracy but as the cost is not taken into account therefore, the resultant solutions are not cost-effective. Based on the comparative analysis, it can be seen that proposed techniques produce solutions for the CKD diagnosis problem which are accurate and cost-effective. The proposed approaches selected a final feature subset for the CKD dataset by retaining around 1/4 th of the original features, decreasing the cost by a factor of 7.42 of the original feature set, and achieving comparable average accuracy as that of other methods in this study. This research can be extended in a number of directions such as we used a classifier-ensemble to account for feature interaction. Although this approach provided promising feature weights but the overall running time of the scoring functions can be reduced by employing lightweight filter techniques. Furthermore, cost can be modeled as a multi-objective function along with the error rate and hence a number of candidate solutions can be generated for the decision maker for an informed decision making. Another important direction worth exploring is to treatment multiple features, collectively. In this study, each feature has its own cost, while, in the medical domain, it is generally the case that data acquired from multiple features are clubbed together under different medical tests.