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A Decision Tree on Data Mining Framework for Recognition of Chronic Kidney Disease

A Decision Tree on Data Mining Framework for Recognition of Chronic Kidney Disease
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Author(s): Ravindra B. V. (SOIS, Manipal Academy of Higher Education, Manipal, India), Sriraam N. (Centre for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology, India) and Geetha M. (Department of CSE, MIT, Manipal Academy of Higher Education, Manipal, India)
Copyright: 2020
Pages: 18
Source title: Biomedical and Clinical Engineering for Healthcare Advancement
Source Author(s)/Editor(s): N. Sriraam (Ramaiah Institute of Technology, India)
DOI: 10.4018/978-1-7998-0326-3.ch005

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Abstract

The term chronic kidney disease (CKD) refers to the malfunction of the kidney and its failure to remove toxins and other waste products from blood. Typical symptoms of CKD include color change in urine, swelling due to fluids staying in tissue, itching, flank pain, and fatigue. Timely intervention is essential for early recognition of CKD as it affects more than 10 million people in India. This chapter suggests a decision tree-based data mining framework to recognize CKD from Non chronic kidney disease (NCKD). Data sets derived from open source UCI repository was considered. Unlike earlier reported work, this chapter applies the decision rules based on the clustered data through k-means clustering process. Four cluster groups were identified and j48 pruned decision tree-based automated rules were formatted. The performance of the proposed framework was evaluated in terms of sensitivity, specificity, precision, and recall. A new quantitative measure, relative performance, and MCC were introduced which confirms the suitability of the proposed framework for recognition of CKD from NCKD.

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