Abstract
In the past decade, the rapid growth of digital data and global accessibility through the modern internet has seen a massive rise in machine learning research. In proportion to it, the medical data has also seen a massive surge of expansion. With the availability of structured clinical data, researchers have attracted scores to study clinical disease detection automation with machine learning and data mining. Chronic Kidney Disease (CKD), also known as the renal disorder, has been such a field of study for quite some time now. Therefore, our research aims to study the automated detection of chronic kidney disease using several machine learning classifiers with clinical data. The purpose of this research work is to diagnose kidney disease using a number of machine learning algorithms such as the Support Vector Machine (SVM) and the Bayesian Network (BN) and to select the most effective one to assess the extent of CKD patients. The amount of expertise in the medical field in relation to CKD is limited. Many patients have to wait a long to get their test results. The experience of medical staff is declining in value. Upon retirement, new employees replace them. It helps professional doctors or medical staff in their diagnosis of CKD. This paper’s primary purpose is to present a clear view of Chronic Kidney Disease (CKD), its symptoms, and the process of early detection that may help humanity be safe from this life-threatening disease.
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Pathak, A., Asma Gani, M., Tasin, A.H., Sania, S.N., Adil, M., Akter, S. (2021). Chronic Kidney Disease (CKD) Prediction Using Data Mining Techniques. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_82
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