relation: https://khub.utp.edu.my/scholars/8722/
title: Predicting dengue incidences using cluster based regression on climate data
creator: Mathulamuthu, S.S.
creator: Asirvadam, V.S.
creator: Dass, S.C.
creator: Gill, B.S.
creator: Loshini, T.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018949872&doi=10.1109%2fICCSCE.2016.7893579&partnerID=40&md5=3b7ac3e2b067966b8e66d874bb6819c8
relation: 10.1109/ICCSCE.2016.7893579
identifier: 10.1109/ICCSCE.2016.7893579