The Classification Performance using Support Vector Machine for Endemic Dengue Cases

Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken stud...

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Published in:Journal of Physics: Conference Series
Main Author: Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
Format: Conference paper
Language:English
Published: Institute of Physics Publishing 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086720184&doi=10.1088%2f1742-6596%2f1496%2f1%2f012006&partnerID=40&md5=e4e3dce30ec143875accc6a75b7d582c
id 2-s2.0-85086720184
spelling 2-s2.0-85086720184
Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
The Classification Performance using Support Vector Machine for Endemic Dengue Cases
2020
Journal of Physics: Conference Series
1496
1
10.1088/1742-6596/1496/1/012006
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086720184&doi=10.1088%2f1742-6596%2f1496%2f1%2f012006&partnerID=40&md5=e4e3dce30ec143875accc6a75b7d582c
Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken study includes data on dengue cases provided by the Health Department in Kelantan, Malaysia. Data scaling were applied to normalize the range of features before being fed into the training model. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear function. The SVM with RBF kernel function was superior to the other techniques because it obtains the highest prediction accuracy of 85%. The polynomial is an alternative model that can achieve a high prediction performance in terms of sensitivity (76%) and specificity (87%). © 2020 Published under licence by IOP Publishing Ltd.
Institute of Physics Publishing
17426588
English
Conference paper
All Open Access; Gold Open Access
author Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
spellingShingle Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
The Classification Performance using Support Vector Machine for Endemic Dengue Cases
author_facet Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
author_sort Nordin N.I.; Mohd Sobri N.; Ismail N.A.; Zulkifli S.N.; Abd Razak N.F.; Mahmud M.
title The Classification Performance using Support Vector Machine for Endemic Dengue Cases
title_short The Classification Performance using Support Vector Machine for Endemic Dengue Cases
title_full The Classification Performance using Support Vector Machine for Endemic Dengue Cases
title_fullStr The Classification Performance using Support Vector Machine for Endemic Dengue Cases
title_full_unstemmed The Classification Performance using Support Vector Machine for Endemic Dengue Cases
title_sort The Classification Performance using Support Vector Machine for Endemic Dengue Cases
publishDate 2020
container_title Journal of Physics: Conference Series
container_volume 1496
container_issue 1
doi_str_mv 10.1088/1742-6596/1496/1/012006
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086720184&doi=10.1088%2f1742-6596%2f1496%2f1%2f012006&partnerID=40&md5=e4e3dce30ec143875accc6a75b7d582c
description Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken study includes data on dengue cases provided by the Health Department in Kelantan, Malaysia. Data scaling were applied to normalize the range of features before being fed into the training model. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear function. The SVM with RBF kernel function was superior to the other techniques because it obtains the highest prediction accuracy of 85%. The polynomial is an alternative model that can achieve a high prediction performance in terms of sensitivity (76%) and specificity (87%). © 2020 Published under licence by IOP Publishing Ltd.
publisher Institute of Physics Publishing
issn 17426588
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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