## 1 Introduction  
Chronic kidney disease (CKD) is an ailment that affects the functionality of a kidney in the body. Generally, CKD is divided into multiple stages in which the later stages are denoted as a renal failure when the kidney is unable to perform its functions of blood purification and balancing minerals in the body [1]. In the case of end-stage renal failure, hemodialysis is performed to supplant the kidney function. This intervention provides a temporary solution to the problem. Hence, it is of paramount importance that the CKD is detected at earlier stages where it can be addressed through medication and lifestyle changes [2]. CKD is a highly prevalent disease, according to an estimate one in nine Korean adults suffer from kidney disease [3]. Likewise, around 2.5-11.2% of the adult population in Europe also suffer from it, while around 59% of all the American adult population is at a high risk of developing kidney disease at some point [4, 5]. The high incidence and prevalence of CKD are attributed to its late diagnosis, especially in developing countries [6]. In the domain of medical data mining, several intelligent clinical decision support systems are designed which tend to automate the diagnosis process [6; 7]. These decision systems employ machine learning techniques that assist physicians in the diagnosis and treatment of CKD in an efficient manner [6; 8; 9]. Based on a number of important indicators such as blood pressure, albumin levels, blood and urea tests, potassium, and other comorbidities, e.g., diabetes, cardiovascular disease, etc., a patient is comprehensively assessed for CKD and its progression. As the earlier diagnosis of the disease onset can improve the chances of patients to favorably respond to treatment, therefore, most of the automated systems are optimized for enhancing the overall accuracy of the model [8; 10]. It is noted by [PERSON] et al. [7] that medical decision systems that solely focus on predictive performance are far from the field reality and hence are not unanimously approved by physicians. In this regard, the interpretability of the classification model is stipulated as one of the important requirements among others for a successful medical decision system [7; 10]. Similarly, the cost factor as a practicability concern for medical decision systems recently gained traction in the medical data mining community [6; 11; 12; 13]. Therefore, one of the key research directions pursued by the research community is to design decision systems that are accurate, interpretable, and cost-effective. In a number of studies performed on CKD diagnosis, decision tree models consistently produced results with high predictive accuracy [8; 9; 12]. Hence, the main impetus for using tree-based models in an ensemble technique is two-fold. Firstly, tree models are easy to interpret by the domain experts, therefore, in domains such as medical diagnosis, it is desirable to assess the validity of the classification model through visual inspection [7; 10; 14]. Secondly, tree-models that are based on bagging and boosting techniques tend to produce highly accurate classifiers on small to medium datasets [6; 8; 9; 15]. Hence, tree models are suitable approaches for considering in an ensemble for a CKD dataset, as they can cater to both types of requirements i.e., interpretability and accuracy. Moreover, feature selection is becoming an essential task in building classification models where the objective is to select a subset of useful features [6; 8; 15; 16; 17]. The notion of usefulness is based on the worth of a feature in a dataset in terms of its relevancy and redundancy. There are generally three approaches for feature selection i.e., filter-based approach, wrapper-based approach, and embedded approach [16; 18; 19]. In the case of filter approaches, the worth of a feature is evaluated through univariate statistical approaches such as Chi-Square, Gini index, information gain, etc. Therefore, feature ranking techniques fall into the filter category.