Predictive models for dengue outbreak using multiple rulebase classifiers

• Published in: Proceedings of the 2011 International Conference on Electrical Engineering and Informatics pp. 1 - 6
• Main Authors: Bakar, A. A., Kefli, Z., Abdullah, S., Sahani, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Accuracy; associative classification; Bayesian methods; Classification algorithms; Data mining; Data models; decision tree; Decision trees; Dengue outbreak; multiple rule based classifier; Naive Bayes; Predictive models; rough set
• ISBN: 1457707535; 9781457707537
• ISSN: 2155-6822
• DOI: 10.1109/ICEEI.2011.6021830

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.