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

doi:10.1088/1742-6596/1255/1/012024

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 journal › Conference article › peer-review

20 Citations (Scopus)

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.

Original language	English
Article number	012024
Journal	Journal of Physics: Conference Series
Volume	1255
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/1255/1/012024
Publication status	Published - 6 Sept 2019
Event	1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018 - Parapat, North Sumatera, Indonesia Duration: 10 Oct 2018 → 12 Oct 2018

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10.1088/1742-6596/1255/1/012024

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title = "Chronic Kidney Disease Prediction by Using Different Decision Tree Techniques",

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.",

author = "Pasadana, {I. A.} and D. Hartama and M. Zarlis and Sianipar, {A. S.} and A. Munandar and S. Baeha and Alam, {A. R.M.}",

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AU - Pasadana, I. A.

AU - Hartama, D.

AU - Zarlis, M.

AU - Sianipar, A. S.

AU - Munandar, A.

AU - Baeha, S.

AU - Alam, A. R.M.

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2019/9/6

Y1 - 2019/9/6

N2 - 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.

AB - 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.

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T2 - 1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018

Y2 - 10 October 2018 through 12 October 2018

ER -

Chronic Kidney Disease Prediction by Using Different Decision Tree Techniques

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