Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables

Shermon S. Mathulamuthu; Vijanth S. Asirvadam; Sarat C. Dass; Balvinder S. Gill

doi:10.1109/SPC.2016.7920705

Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables

Shermon S. Mathulamuthu, Vijanth S. Asirvadam, Sarat C. Dass, Balvinder S. Gill

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2016 IEEE Conference on Systems, Process and Control (ICSPC)
Publisher	IEEE
Pages	64-69
Number of pages	6
ISBN (Electronic)	9781509011810
DOIs	https://doi.org/10.1109/SPC.2016.7920705
Publication status	Published - 8 May 2017

Keywords

Dengue Incidences
Machine Learning
K-means Cluster
Averaged Silhouette width
Regression

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SPC.2016.7920705

Cite this

@inproceedings{cc2fc13b17a64322a329324aff0d7451,

title = "Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables",

abstract = "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.",

keywords = "Dengue Incidences, Machine Learning, K-means Cluster, Averaged Silhouette width, Regression",

author = "Mathulamuthu, {Shermon S.} and Asirvadam, {Vijanth S.} and Dass, {Sarat C.} and Gill, {Balvinder S.}",

year = "2017",

month = may,

day = "8",

doi = "10.1109/SPC.2016.7920705",

language = "English",

pages = "64--69",

booktitle = "2016 IEEE Conference on Systems, Process and Control (ICSPC)",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables

AU - Mathulamuthu, Shermon S.

AU - Asirvadam, Vijanth S.

AU - Dass, Sarat C.

AU - Gill, Balvinder S.

PY - 2017/5/8

Y1 - 2017/5/8

N2 - 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.

AB - 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.

KW - Dengue Incidences

KW - Machine Learning

KW - K-means Cluster

KW - Averaged Silhouette width

KW - Regression

U2 - 10.1109/SPC.2016.7920705

DO - 10.1109/SPC.2016.7920705

M3 - Conference contribution

SP - 64

EP - 69

BT - 2016 IEEE Conference on Systems, Process and Control (ICSPC)

PB - IEEE

ER -

Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables

Abstract

Keywords

UN SDGs

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