Predictive models for dengue outbreak using multiple rulebase classifiers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

The paper aims to develop the predictive models for dengue outbreak detection using Multiple Rule Based Classifiers. The rule based classifiers used are the Decision Tree, Rough Set Classifier, Naive Bayes, and Associative Classifier. Dengue fever (DF) and dengue hemorrhagic fever (DHF) have been continuously becoming a public health related issues in Malaysia and growing pandemic as reported by World Health Organization (WHO). It is important for the government to able to make early detection for dengue outbreak. Thus, to improve early detection of the dengue outbreak and making such strategic planning and decision, being able to predict or forecast the possible dengue outbreak in an area is critically important. The purpose of the classification modelling is to build a predictive model for predicting the dengue outbreak. Since to date there is no research uses this data for predictive modelling, several classifiers are investigated to study the performance of various rule based classifiers individually and the combination of the classifiers. The experimental results show that the multiple classifiers are able produce better accuracy (up to 70%) with more quality rules compared to the single classifier.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 - Bandung
Duration: 17 Jul 201119 Jul 2011

Other

Other2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011
CityBandung
Period17/7/1119/7/11

Fingerprint

Classifiers
Strategic planning
Public health
Decision trees
Health

Keywords

  • associative classification
  • decision tree
  • Dengue outbreak
  • multiple rule based classifier
  • Naive Bayes
  • rough set

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering

Cite this

Abu Bakar, A., Kefli, Z., Abdullah, S., & Sahani, M. (2011). Predictive models for dengue outbreak using multiple rulebase classifiers. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 [6021830] https://doi.org/10.1109/ICEEI.2011.6021830

Predictive models for dengue outbreak using multiple rulebase classifiers. / Abu Bakar, Azuraliza; Kefli, Zuriyah; Abdullah, Salwani; Sahani, Mazrura.

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011. 6021830.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abu Bakar, A, Kefli, Z, Abdullah, S & Sahani, M 2011, Predictive models for dengue outbreak using multiple rulebase classifiers. in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011., 6021830, 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011, Bandung, 17/7/11. https://doi.org/10.1109/ICEEI.2011.6021830
Abu Bakar A, Kefli Z, Abdullah S, Sahani M. Predictive models for dengue outbreak using multiple rulebase classifiers. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011. 6021830 https://doi.org/10.1109/ICEEI.2011.6021830
Abu Bakar, Azuraliza ; Kefli, Zuriyah ; Abdullah, Salwani ; Sahani, Mazrura. / Predictive models for dengue outbreak using multiple rulebase classifiers. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011.
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