An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles

Saba Bashir; Usman Qamar; Farhan Hassan Khan; M. Younus Javed

doi:10.1109/FIT.2014.50

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles

Saba Bashir
, Usman Qamar
, Farhan Hassan Khan
, M. Younus Javed

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

53 Scopus citations

Abstract

Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

Original language	English
Title of host publication	Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	226-231
Number of pages	6
ISBN (Electronic)	9781479975051
DOIs	https://doi.org/10.1109/FIT.2014.50
State	Published - 2014
Externally published	Yes

Publication series

Name	Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Adaboost
Bagging
Bayesian boosting
Boosting
Decision trees
Diabetes
Ensemble Classifiers
Stacking

ASJC Scopus subject areas

Information Systems

Access to Document

10.1109/FIT.2014.50

Cite this

Bashir, S., Qamar, U., Khan, F. H., & Javed, M. Y. (2014). An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles. In Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014 (pp. 226-231). Article 7118404 (Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FIT.2014.50

Bashir, Saba ; Qamar, Usman ; Khan, Farhan Hassan et al. / An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles. Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 226-231 (Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014).

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title = "An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, \& CART Ensembles",

abstract = "Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.",

keywords = "Adaboost, Bagging, Bayesian boosting, Boosting, Decision trees, Diabetes, Ensemble Classifiers, Stacking",

author = "Saba Bashir and Usman Qamar and Khan, \{Farhan Hassan\} and Javed, \{M. Younus\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",

year = "2014",

doi = "10.1109/FIT.2014.50",

language = "English",

series = "Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bashir, S, Qamar, U, Khan, FH & Javed, MY 2014, An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles. in Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014., 7118404, Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014, Institute of Electrical and Electronics Engineers Inc., pp. 226-231. https://doi.org/10.1109/FIT.2014.50

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles. / Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan et al.
Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 226-231 7118404 (Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

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KW - Bayesian boosting

KW - Boosting

KW - Decision trees

KW - Diabetes

KW - Ensemble Classifiers

KW - Stacking

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Bashir S, Qamar U, Khan FH, Javed MY. An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles. In Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 226-231. 7118404. (Proceedings - 12th International Conference on Frontiers of Information Technology, FIT 2014). doi: 10.1109/FIT.2014.50

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this