Abstract
Early detection and proper management of Chronic Kidney Disease (CKD) are solicited for augmenting survivability due to fact that CKD is one of the life-threatening diseases. The UCI’s CKD dataset which is selected for this study is consisting of attributes like age, blood pressure, specific grativity, albumin, sugar, red blood cells, plus cell, pus cell clumps, bacteria, blood glucose random, and blood urea. The main purpose of this work is to calculate the performance of various decision tree algorithm and compare their performance. The decision tree techniques used in this study are DecisionStump, HoeffdingTree, J48, CTC, J48graft, LMT, NBTree, RandomForest, RandomTree, REPTree, and SimpleCart. Hence, the results show that RandomForest serves the highest accuracy in identifying CKD.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
