## 2 Literature Review
They reported that RF achieved the highest accuracy of 99.75% for the CKD diagnosis, while logistic regression (LG) was able to produce an accuracy of 98.95%. Afterward, the authors proposed an integrated model that employed both the aforementioned classifiers along with the perceptron and subsequently produced an accuracy of 99.83% using the integrated approach. [PERSON] et al. [6] performed a comprehensive study on CKD diagnosis using various machine learning algorithms. The authors reported the highest accuracy achieved by decision tree-based models in the pool of candidate models which included Naive Bayes (NB), SVM, ANN, KNN. We provided a general overview of the feature selection techniques and classification algorithms applied to CKD diagnosis and it can be observed that decision tree-based models are one of the popular modeling approaches for the CKD diagnosis. Our proposed approach is based on feature ranking therefore we herein mention a few studies which addressed the problem of automatically selecting an appropriate threshold value using heuristics. Most of the studies opt for a fixed threshold value for retaining a set of top features [32; 33; 34]. But as it is observed that a fixed threshold value may over-select or under-select an appropriate number of features [20; 29; 35; 36]. Authors in [37] used data complexity measures for selecting a threshold value while authors in [38] used a minimum union method to combine multiple rankings and produced promising results on high dimensional datasets [39]. [PERSON] and [PERSON] [17] performed a detailed study regarding combining multiple feature selection methods for stock prediction problem. Authors reported higher predictive accuracy over the ANN classifier based on the multi-point interaction among Principal Component Analysis (PCA), Genetic Algorithm (GA), and Decision Tree (DT) feature sets, among other combination strategies. [PERSON] et al. [40] proposed an ensemble feature selection technique in which the authors combine partial results from multiple filter measures. Subsequently, 1/3 rd of features from each feature selection method are retained. Consolidated feature subset is obtained through the intersection of the candidate feature subsets. Several threshold selection approaches are reported in the literature that are primarily geared towards optimizing the accuracy of the classification task. But the aforementioned approaches cannot take into account valuable meta-information associated with the feature set, such as cost of data acquisition. It is important to note that the cost-free feature selection approaches may also result in cost reduction but such a cost reduction would be effectively unintentional since the cost-free feature selection methods are oblivious to the cost meta-data associated with the features. Our proposed approaches are based on explicitly taking into account the cost factor along with the feature worth. In this regard, we show that an automatic threshold value can be selected based on the feature-cost interaction curve, as shown in Figure 1b, which results in a feature set that is both useful and cost-effective. In the following section, we elaborate on the proposed methodology for computing feature weightage along with the application of a threshold value in arriving at a final feature set.