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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 Sumon, Md. Shariful Hasan (2015) Single decision tree classifiers' accuracy on medical data. In: 5th International Conference on Computing and Informatics (ICOCI 2015), 11-13 Aug. 2015, Istanbul, Turkey. (pp. 671-676).


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.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Medicine and Health Science
Publisher: School of Computing, Universiti Utara Malaysia
Keywords: Decision tree classifier; Machine learning algorithm; Decision tree evaluation
Depositing User: Nabilah Mustapa
Date Deposited: 22 Feb 2018 03:17
Last Modified: 22 Feb 2018 03:17
URI: http://psasir.upm.edu.my/id/eprint/59080
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