Comparative Analysis of Various Tree Classifier Algorithms for Disease Datasets

Comparative Analysis of Various Tree Classifier Algorithms for Disease Datasets
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© 2021 by IJETT Journal
Volume-69 Issue-6
Year of Publication : 2021
Authors : Sajithra. N, Dr.D.Ramyachitra
DOI :  10.14445/22315381/IJETT-V69I6P202

How to Cite?

Sajithra. N, Dr.D.Ramyachitra, "Comparative Analysis of Various Tree Classifier Algorithms for Disease Datasets," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 8-13, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I6P202

Abstract
Tree-Based Classification technique is one of the commonly used techniques called White box classification. It targets foreseeing to the having a place of cases or articles in the classes of a particular variable from their estimations on at least one prescient factor. This research work analyzes the concert of five tree-based classification algorithms, namely Decision Stump, J48, Logistic Model Tress (LMT), Random Forest, and REPTree. Various disease datasets such as breast cancer, Pima diabetes, and hypothyroid are utilized for calculating the performance of the classification algorithms by applying the 10-fold cross-validation parameter based on the given class label. Finally, the comparative analysis is held out, using the classification accuracy, kappa value, performance factors, and the error rate measures on all of the algorithms. From the experimental outcomes, it is derived that the LMT provides better results for all the disease datasets than the existing algorithms such as Decision Stump, J48, Random Forest, and REPTree.

Keywords
Decision Stump, J48, LMT, Random Forest, REPTree.

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