Predicting Dengue Incidences Using Cluster Based Regression on Climate Data

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 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 languageEnglish
Title of host publication2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)
PublisherIEEE
Pages245-250
Number of pages6
ISBN (Electronic)9781509011780
DOIs
Publication statusPublished - 6 Apr 2017

Keywords

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

Cite this

Mathulamuthu, S. S., Asirvadam, V. S., Dass, S. C., Gill, B. S., & Loshini, T. (2017). Predicting Dengue Incidences Using Cluster Based Regression on Climate Data. In 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 245-250). IEEE. https://doi.org/10.1109/ICCSCE.2016.7893579