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. These decision systems employ machine learning techniques that assist physicians in the diagnosis and treatment of CKD in an efficient manner. In the domain of medical data mining, several intelligent clinical decision support systems are designed which tend to automate the diagnosis process. The high incidence and prevalence of CKD are attributed to its late diagnosis, especially in developing countries. 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. CKD is a highly prevalent disease, according to an estimate one in nine Korean adults suffer from kidney disease. Hence, it is of paramount importance that the CKD is detected at earlier stages where it can be addressed through medication and lifestyle changes. This intervention provides a temporary solution to the problem. In the case of end-stage renal failure, hemodialysis is performed to supplant the kidney function. 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. Based on the extensive experimentation, it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.Ĭhronic kidney disease (CKD) is an ailment that affects the functionality of a kidney in the body. Furthermore, it is demonstrated that the proposed techniques select around 1/4th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. The proposed approaches were also evaluated against several comparative techniques. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non tree-based classification models. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. In this research, we proposed two techniques for cost-sensitive feature ranking. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we argue that, unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare.
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