Predicting Dengue Incidences Using Cluster Based Regression on Climate Data

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

doi:10.1109/ICCSCE.2016.7893579

Predicting Dengue Incidences Using Cluster Based Regression on Climate Data

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

School of Mathematical & Computer Sciences

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

7 Citations (Scopus)

Abstract

Dengue incidence prediction models are very important at present as the dengue cases becoming a major health issue in tropical and subtropical countries. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). In order to curb this problem, it is important for the government to create a predictive system so that precaution steps could be taken. This study builds a dengue incidence prediction model to avoid epidemic using climate models in real time. Data mining techniques such as clustering and multiple regression are used to model the data in order to get the best fitting regression curve. In the next step, a real time adaptive computation software will be developed that could predict the dengue incidences immediately.

Original language	English
Title of host publication	2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)
Publisher	IEEE
Pages	245-250
Number of pages	6
ISBN (Electronic)	9781509011780
DOIs	https://doi.org/10.1109/ICCSCE.2016.7893579
Publication status	Published - 6 Apr 2017

Keywords

Dengue Incidences
Real-Time
Machine Learning
K-means Cluster
Multiple Regression

UN SDGs

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

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10.1109/ICCSCE.2016.7893579

Cite this

@inproceedings{06ea58e623c94795bcb71fbe4b430102,

title = "Predicting Dengue Incidences Using Cluster Based Regression on Climate Data",

abstract = "Dengue incidence prediction models are very important at present as the dengue cases becoming a major health issue in tropical and subtropical countries. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). In order to curb this problem, it is important for the government to create a predictive system so that precaution steps could be taken. This study builds a dengue incidence prediction model to avoid epidemic using climate models in real time. Data mining techniques such as clustering and multiple regression are used to model the data in order to get the best fitting regression curve. In the next step, a real time adaptive computation software will be developed that could predict the dengue incidences immediately.",

keywords = "Dengue Incidences, Real-Time, Machine Learning, K-means Cluster, Multiple Regression",

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

year = "2017",

month = apr,

day = "6",

doi = "10.1109/ICCSCE.2016.7893579",

language = "English",

pages = "245--250",

booktitle = "2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)",

publisher = "IEEE",

address = "United States",

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TY - GEN

T1 - Predicting Dengue Incidences Using Cluster Based Regression on Climate Data

AU - Mathulamuthu, Shermon S.

AU - Asirvadam, Vijanth S.

AU - Dass, Sarat C.

AU - Gill, Balvinder S.

AU - Loshini, T.

PY - 2017/4/6

Y1 - 2017/4/6

N2 - Dengue incidence prediction models are very important at present as the dengue cases becoming a major health issue in tropical and subtropical countries. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). In order to curb this problem, it is important for the government to create a predictive system so that precaution steps could be taken. This study builds a dengue incidence prediction model to avoid epidemic using climate models in real time. Data mining techniques such as clustering and multiple regression are used to model the data in order to get the best fitting regression curve. In the next step, a real time adaptive computation software will be developed that could predict the dengue incidences immediately.

AB - Dengue incidence prediction models are very important at present as the dengue cases becoming a major health issue in tropical and subtropical countries. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). In order to curb this problem, it is important for the government to create a predictive system so that precaution steps could be taken. This study builds a dengue incidence prediction model to avoid epidemic using climate models in real time. Data mining techniques such as clustering and multiple regression are used to model the data in order to get the best fitting regression curve. In the next step, a real time adaptive computation software will be developed that could predict the dengue incidences immediately.

KW - Dengue Incidences

KW - Real-Time

KW - Machine Learning

KW - K-means Cluster

KW - Multiple Regression

U2 - 10.1109/ICCSCE.2016.7893579

DO - 10.1109/ICCSCE.2016.7893579

M3 - Conference contribution

SP - 245

EP - 250

BT - 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

PB - IEEE

ER -

Predicting Dengue Incidences Using Cluster Based Regression on Climate Data

Abstract

Keywords

UN SDGs

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