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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 197-202

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Olawumi OLASUNKANMI fingerprint•
Odunayo OLANLOYE fingerprint•
Abdulquadri ADEGBIJI fingerprint
Publication DateDecember 31, 2020
Volume / IssueVol. 8, No. 4 (pp. 197-202)
subject

Abstract

The world is now in the era of big data and processing, and exploring the data has become one of the significant challenges. Hence, researchers have done a lot to analyse these data in the health sector to enhance disease detection and classification using artificial intelligence and ML principles. Kidney disease is one of the terrible conditions in which its late detection has sent many people to untimely graves. ML classifiers have been employed in many dimensions to classify heart disease, but, existing works have not explored the variants of each method for selection of best model parameters. An attempt is being made in this research to study the behaviour of three (3) variants each from two(2) tree-based models in the classification of Kidney Disease. Three of the variants are Complex, Medium and Simple models of Decision tree classifier and the other one are Boosted, Bagged and RUSBoosted of Ensemble Classifiers. Using MATLAB for implementation, the model performance established that the accuracy of Ensemble Classifier (Bagged tree model) is the best, concerning the speed, Decision tree (Complex and Simple tree models have the same and highest value). Hence, the two are the best. In terms of training time, Decision tree(Simple tree) has the least time and therefore the best.
Keywords:Ensemble ClassifierDecision TreesKidney DiseasesMachine Learning

Author Affiliations

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References (17)

Cited Literature
  1. 1

    A. F. Kana, "Introduction to Artificial Intelligence Lecture Note Series", 2016.

  2. 2

    A. Tarun. Towards Data Science. https://tpwardsdatascience.com/advanced_ensemble_class, 2019.

  3. 3

    A. Wala and A. Noora, Abdulrahman, "Missing Data Classification of Chronic Kidney Disease", International Journal of Data Mining and Knowledge Management process (IJDKP), vol 7., pp. 5-6, November 2017.

  4. 4

    A.K. Shrivas and K. S. Sanat, "A proposed ensemble model with feature selection technique for classification of chronic kidney disease", International Journal of Engineering and Advanced Technology (IJEAT), vol 9, 2019. DOI: DOI: 10.35940/ijeat.A2207.129219

    open_in_newCrossref / DOI: 10.35940/ijeat.A2207.129219
  5. 5

    B. Basma, M. Hajar and H. Abdelkrim, "Performance of Data Mining Technique to Predict In Health Care In Health Care Case Study: Chronic Kidney Failure Disease", International Journal of Database Management System. (IJDMS), vol 8, June 2016.

  6. 6

    H. Ned, "Introduction to decision trees and random forest", American Museum of Natural History's Center for Biodiversity and Conservation, 2019.

  7. 7

    K. S. Sanjay, M. Adeel, F. Ahmad and J. Vivekanand, "A Clinical Database of Kidney Disease", BMC Nephrology, vol 13, pp. 1471- 2369, 2012.

  8. 8

    M. D. Basar and A. Akan, "Detection of chronic kidney disease by using ensemble classifiers", 10th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, 2017, pp. 544-547.

  9. 9

    O. A. Jongbo, A.O. Adetunbi, B.O. Ogunrinde, B.B. Ajisafe, "Development of an ensemble approach to chronic kidney disease diagnosis", Scientific Africa, 2020. DOI: https://doi.org/10.1016/j.sciaf.2020.e00456

    open_in_newCrossref / DOI: 10.1016/j.sciaf.2020.e00456
  10. 10

    S. Jyoti, R.C. Gangwar and M. Molute, "A novel detection for Kidney Disease using Improved Support Vector Machine" International Journal of Latest Trends in Engineering and Technology vol 8, pp 114–121, 2015. DOI: http://dx.doi.org 10.21172//.81.015.

    open_in_newCrossref / DOI: 10.21172//.81.015
  11. 11

    S. P. Senthil and P. Anitha, "Comparison of feature selection methods for chronic kidney dataset using data mining classification analytical model" International Research Journal of Engineering and Technology (IRJET), vol 6 (2), 2019.

  12. 12

    S. Ramya and S. Radha, "Diagnosis of chronic kidney disease using machine learning algorithm" International Journal of Innovative Research in Computer and Communication Engineering, vol 4, January 2016.

  13. 13

    S. Vijayarani and S. Dhayanand, "Kidney disease prediction using SVM and ANN algorithm", International Journal of Computing and Business Research (IJBCR), vol 6, 2015.

  14. 14

    U. N. Dulhare andM. Ayesha, "Extraction of action rules for chronic kidney disease using Naïve Bayes classifier" In 2016 IEEE InternationalConference on Computational Intelligence and Computing Research (ICCIC), pp. 1-5, 2016.

  15. 15

    www.mathworks.com/help/stats/decision-trees.html. MatLab Documentation

  16. 16

    Z. Sirage and P. Shruti, "Prediction of chronic kidney disease using data mining features selection and ensemble method", WSEAS Transactions on Information Science Applications, vol 15, pp 168- 176, 2018.

  17. 17

    https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease. Data Source

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How to Cite This Article

O. O., O. O., A. A. (2020). Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases. International Journal of Applied Methods in Electronics and Computers, 197-202. https://doi.org/10.18100/ijamec.792863