Feature selection algorithms for Malaysian dengue outbreak detection model

Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been co...

Full description

Bibliographic Details
Main Authors:	Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2017
Online Access:	http://journalarticle.ukm.my/10678/ http://journalarticle.ukm.my/10678/ http://journalarticle.ukm.my/10678/1/10%20Husam%20I.S.pdf

id	ukm-10678
recordtype	eprints
spelling	ukm-106782017-09-20T09:20:32Z http://journalarticle.ukm.my/10678/ Feature selection algorithms for Malaysian dengue outbreak detection model Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali, Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies. Penerbit Universiti Kebangsaan Malaysia 2017-02 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/10678/1/10%20Husam%20I.S.pdf Husam I.S. Abuhamad, and Azuraliza Abu Bakar, and Suhaila Zainudin, and Mazura Sahani, and Zainudin Mohd Ali, (2017) Feature selection algorithms for Malaysian dengue outbreak detection model. Sains Malaysiana, 46 (2). pp. 255-265. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol46num2_2017/contentsVol46num2_2017.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies.
format	Article
author	Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali,
spellingShingle	Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali, Feature selection algorithms for Malaysian dengue outbreak detection model
author_facet	Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali,
author_sort	Husam I.S. Abuhamad,
title	Feature selection algorithms for Malaysian dengue outbreak detection model
title_short	Feature selection algorithms for Malaysian dengue outbreak detection model
title_full	Feature selection algorithms for Malaysian dengue outbreak detection model
title_fullStr	Feature selection algorithms for Malaysian dengue outbreak detection model
title_full_unstemmed	Feature selection algorithms for Malaysian dengue outbreak detection model
title_sort	feature selection algorithms for malaysian dengue outbreak detection model
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2017
url	http://journalarticle.ukm.my/10678/ http://journalarticle.ukm.my/10678/ http://journalarticle.ukm.my/10678/1/10%20Husam%20I.S.pdf
first_indexed	2023-09-18T19:58:08Z
last_indexed	2023-09-18T19:58:08Z
_version_	1777406667562942464

Feature selection algorithms for Malaysian dengue outbreak detection model

Similar Items