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Single decision tree classifiers' accuracy on medical data


Hasan, Md Rajib and Abu Bakar, Nur Azzah and Siraj, Fadzilah and Sainin, Mohd Shamrie and Hasan, Shariful (2015) Single decision tree classifiers' accuracy on medical data. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey.

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Abstract

Decision tree is one of the classification techniques for classifying sequential decision problems such as those in medical domain.This paper discusses an evaluation study on different single decision tree classifiers.There are various single decision tree classifiers which have been extensively applied in medical decision making; each of these classifies the data with different accuracy rate.Since accuracy is crucial in medical decision making, it is important to identify a classifier with the best accuracy.The study examines the performance of fourteen single decision tree classifiers on three medical data sets, i.e. Wisconsin’s breast cancer data sets, Pima Indian diabetes data sets and hepatitis data sets.All classifiers were trained and tested using WEKA and cross validation. The results revealed that classifiers such as FT, LMT, NB tree, Random Forest and Random Tree are the five best single classifiers as they constantly provide better accuracy in their classifications.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN No: 978-967-0910-02-4 Jointly organized by: Universiti Utara Malaysia & Istanbul Zaim University
Uncontrolled Keywords: decision tree classifier, machine learning algorithm, decision tree evaluation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Nur Azzah Abu Bakar
Date Deposited: 30 Sep 2015 08:06
Last Modified: 27 Apr 2016 03:33
URI: http://repo.uum.edu.my/id/eprint/15527

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