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Predicting dengue incidences using cluster based regression on climate data

Mathulamuthu, S.S. and Asirvadam, V.S. and Dass, S.C. and Gill, B.S. and Loshini, T. (2017) Predicting dengue incidences using cluster based regression on climate data. Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016 . pp. 245-250.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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. © 2016 IEEE.

Item Type:Article
Impact Factor:cited By 0
ID Code:20106
Deposited By: Ahmad Suhairi
Deposited On:22 Apr 2018 14:41
Last Modified:22 Apr 2018 14:41

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