Single decision tree classifiers' accuracy on medical data
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 a...
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
School of Computing, Universiti Utara Malaysia
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59080/ http://psasir.upm.edu.my/id/eprint/59080/1/PID188.pdf |
| _version_ | 1848853823718686720 |
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| author | Hasan, Md. Rajib Abu Bakar, Nur Azzah Siraj, Fadzilah Sainin, Mohd Shamrie Sumon, Md. Shariful Hasan |
| author_facet | Hasan, Md. Rajib Abu Bakar, Nur Azzah Siraj, Fadzilah Sainin, Mohd Shamrie Sumon, Md. Shariful Hasan |
| author_sort | Hasan, Md. Rajib |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T11:00:06Z |
| format | Conference or Workshop Item |
| id | upm-59080 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:00:06Z |
| publishDate | 2015 |
| publisher | School of Computing, Universiti Utara Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-590802018-02-22T03:17:08Z http://psasir.upm.edu.my/id/eprint/59080/ Single decision tree classifiers' accuracy on medical data Hasan, Md. Rajib Abu Bakar, Nur Azzah Siraj, Fadzilah Sainin, Mohd Shamrie Sumon, Md. Shariful Hasan 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. School of Computing, Universiti Utara Malaysia 2015 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59080/1/PID188.pdf 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). |
| spellingShingle | Hasan, Md. Rajib Abu Bakar, Nur Azzah Siraj, Fadzilah Sainin, Mohd Shamrie Sumon, Md. Shariful Hasan Single decision tree classifiers' accuracy on medical data |
| title | Single decision tree classifiers' accuracy on medical data |
| title_full | Single decision tree classifiers' accuracy on medical data |
| title_fullStr | Single decision tree classifiers' accuracy on medical data |
| title_full_unstemmed | Single decision tree classifiers' accuracy on medical data |
| title_short | Single decision tree classifiers' accuracy on medical data |
| title_sort | single decision tree classifiers' accuracy on medical data |
| url | http://psasir.upm.edu.my/id/eprint/59080/ http://psasir.upm.edu.my/id/eprint/59080/1/PID188.pdf |