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Chronic kidney disease (CKD) is one of the leading medical ailments in developing countries. Due to the limited healthcare infrastructure and the lack of trained human resources, the CKD problem aggravates if it is not addressed in its earlier stages. In this regard, the role of machine learning-based automated diagnosis systems plays a vital role to deal with the CKD problem. In most of the studies conducted on the automated CKD decision modeling, the main emphasis is given to enhancing the predictive accuracy of the system. In this study, we focus on the applicability challenges of automated decision systems taking CKD diagnosis as a case study within the purview of developing countries. In this regard, we propose a cost-sensitive ensemble feature ranking method that takes a more realistic approach to group-based feature selection. Two candidate solutions are proposed for group-based feature selection to meet different objectives. Subsequently, both the candidate solutions are combined into a consolidated solution. It is pertinent to note that it is one of the first studies in which cost-sensitive ensemble feature ranking for non-overlapping groups is successfully demonstrated to achieve the stated objectives i.e. low-cost and high-accuracy solution. Based on an extensive set of experiments, we demonstrate that a cost-effective and accurate solution for the CKD problem can be obtained. The experimentation includes 7 well-known classification algorithms and 8 comparative feature selection methods to show the efficacy of the proposed approach. It is concluded that the applicability of the automated CKD systems can be enhanced by including the cost consideration into the objective space of the solution formulation. Therefore, a trade-off solution can be obtained that is cost-effective and yet accurate enough to serve as a CKD screening system.

Ensemble Feature Ranking for Cost-Based Non-Overlapping Groups: A Case Study of Chronic Kidney Disease Diagnosis in Developing Countries