Mustaffa, Zuriani and Yusof, Yuhanis (2010) A comparison of normalization techniques in predicting dengue outbreak. In: 2010 International Conference on Information and Finance (ICIF 2010) 26-28 November 2010, Kuala Lumpur, Malaysia. IEEE Computer Society, pp. 345-349. ISBN 978-1-4244-9547-4

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Abstract

In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is increasing rapidly in Malaysia, more work need to be done in order to prevent this situation become critical. This includes work on predicting future dengue outbreak. This paper investigates the use of three normalization techniques in predicting dengue outbreak; Min- Max, Z-Score and Decimal Point Normalization. These techniques are incorporated in the LS-SVM and Neural Network (NNM) prediction model respectively. Comparisons of results are made based on prediction accuracy and mean squared error (MSE). Results obtained indicate that the LSSVM is a better prediction model as compared to the NNM.

Item Type:	Book Section
Uncontrolled Keywords:	Least Squares Support Vector Machines; Support Vector Machines; Neural Network Model; Dengue fever
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	01 Jun 2011 04:02
Last Modified:	01 Jun 2011 04:03
URI:	https://repo.uum.edu.my/id/eprint/3163

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