Dengue outbreak prediction: hybrid meta-heuristic model

Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Ernawan, Ferda and Yuhanis, Yusof and Mohamad Farhan, Mohamad Mohsin (2018) Dengue outbreak prediction: hybrid meta-heuristic model. In: 19th IEE/ACIS International Conference On Software Engineering, Artificial Intelligence,Networking And Parallel/Distributed Computing (SNPD 2018), 27 - 29 June 2018 , Busan, Korea. pp. 1-4.. ISBN 978-153865889-5

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Parameter tuning of Leas Squares Support Vector Machines (LSSVM) hyper-parameters, namely regularization parameter and kernel parameters plays a crucial role in obtaining a promising result in prediction task. Any improper values setting of the said hyper-parameters would demote the generalization of LSSVM. Concerning that matter, in this study, Flower Pollination Algorithm (PA), which is relatively new optimization algorithm is hybrid with LSSVM. Here, the FPA is served as an optimization algorithm for LSSVM. The hybrid FPA-LSSVM is later realized for prediction of dengue outbreak in Yogyakarta, Indonesia. Since it was first recognized, until now Dengue Fever (DF) remains as a major concern of public health in community, specifically during the massive outbreaks. A serious infection of dengue can progress into a more critical condition namely Dengue Hemorrhagic Fever (DHF). Therefore, a good prediction model is vital to predict the dengue outbreak cases. By using monthly disease surveillance and meteorological data, the performance of the prediction model is guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE). Findings of the study demonstrate that FPA-LSSVM is able to produce lower error rate compared to the other identified algorithms.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Index by Scopus
Uncontrolled Keywords: Dengue outbreak prediction; Flower Pollination Algorithm; Least Squares Support Vector Machines; Machine Learning
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 15 Nov 2018 08:29
Last Modified: 15 Nov 2018 08:35
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