Chronic Kidney Disease Prediction by Using Different Decision Tree Techniques

I. A. Pasadana, D. Hartama, M. Zarlis, A. S. Sianipar, A. Munandar, S. Baeha, A. R.M. Alam

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Article number012024
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 6 Sept 2019
Event1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018 - Parapat, North Sumatera, Indonesia
Duration: 10 Oct 201812 Oct 2018


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