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Dengue Prediction Using Hierarchical Clustering Methods

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Designing for a Digital and Globalized World (DESRIST 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10844))

Abstract

The occurrence of dengue is rapidly increasing in every year. Considering the welfare of the public, it is essential to have detailed study on the affected areas of dengue and its intensity for the control of disease. This paper uses hierarchical clustering technique to classify the data of dengue cases reported and deaths occurred in various states of India. An agglomerative clustering of ward method is used for clustering. The outcomes are represented in Indian map using shape file with RStudio. The data is predicted for 2018, by logarithmic transformation using linear models of regression. K-Nearest Neighbour algorithm is used for predicting the cluster data for 2018. The results have shown that the frequency of dengue happening or the intensity is considerably reduced in many states.

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Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    S. Vandhana & J. Anuradha

Authors
  1. S. Vandhana
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  2. J. Anuradha
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Correspondence to S. Vandhana .

Editor information

Editors and Affiliations

  1. School of Information Systems and Technology, Claremont Graduate University, Claremont, California, USA

    Samir Chatterjee

  2. Muma College of Business, University of South Florida, Tampa, Florida, USA

    Kaushik Dutta

  3. Department of Management Studies, IIT Madras, Chennai, Tamil Nadu, India

    Rangaraja P. Sundarraj

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Vandhana, S., Anuradha, J. (2018). Dengue Prediction Using Hierarchical Clustering Methods. In: Chatterjee, S., Dutta, K., Sundarraj, R. (eds) Designing for a Digital and Globalized World. DESRIST 2018. Lecture Notes in Computer Science(), vol 10844. Springer, Cham. https://doi.org/10.1007/978-3-319-91800-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-91800-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91799-3

  • Online ISBN: 978-3-319-91800-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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