Cluster based regression model on dengue incidence using dual climate variables

Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine...

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Main Authors: Mathulamuthu, S.S., Asirvadam, V.S., Dass, S.C., Gill, B.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20088 /
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019974663&doi=10.1109%2fSPC.2016.7920705&partnerID=40&md5=adbb72129af5f18a42408f266ee393bb
http://eprints.utp.edu.my/20088/
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spelling utp-eprints.200882018-04-22T14:40:45Z Cluster based regression model on dengue incidence using dual climate variables Mathulamuthu, S.S. Asirvadam, V.S. Dass, S.C. Gill, B.S. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine learning techniques such as clustering and regression is done to compare the sum square of residual (SSE) to conclude which climate variable is giving a big impact on dengue cases. Unsupervised techniques of K-means clustering is done to group the data accordingly. Averaged silhouette width method is used to define the number of K group. Each cluster the regression model is built and SSE is shown in table. Thus through the SSE the model validity can be known. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019974663&doi=10.1109%2fSPC.2016.7920705&partnerID=40&md5=adbb72129af5f18a42408f266ee393bb Mathulamuthu, S.S. and Asirvadam, V.S. and Dass, S.C. and Gill, B.S. (2017) Cluster based regression model on dengue incidence using dual climate variables. Proceedings - 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016 . pp. 64-69. http://eprints.utp.edu.my/20088/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine learning techniques such as clustering and regression is done to compare the sum square of residual (SSE) to conclude which climate variable is giving a big impact on dengue cases. Unsupervised techniques of K-means clustering is done to group the data accordingly. Averaged silhouette width method is used to define the number of K group. Each cluster the regression model is built and SSE is shown in table. Thus through the SSE the model validity can be known. © 2016 IEEE.
format Article
author Mathulamuthu, S.S.
Asirvadam, V.S.
Dass, S.C.
Gill, B.S.
spellingShingle Mathulamuthu, S.S.
Asirvadam, V.S.
Dass, S.C.
Gill, B.S.
Cluster based regression model on dengue incidence using dual climate variables
author_sort Mathulamuthu, S.S.
title Cluster based regression model on dengue incidence using dual climate variables
title_short Cluster based regression model on dengue incidence using dual climate variables
title_full Cluster based regression model on dengue incidence using dual climate variables
title_fullStr Cluster based regression model on dengue incidence using dual climate variables
title_full_unstemmed Cluster based regression model on dengue incidence using dual climate variables
title_sort cluster based regression model on dengue incidence using dual climate variables
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019974663&doi=10.1109%2fSPC.2016.7920705&partnerID=40&md5=adbb72129af5f18a42408f266ee393bb
http://eprints.utp.edu.my/20088/
_version_ 1741196315756331008
score 11.62408

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