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Machine Learning-Based Approaches for Location Based Dengue Prediction: Review

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Fourth International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1041))

Abstract

Dengue is a fast-spreading viral disease which has no preventive medicine. Due to this infectious disease, almost half of the global population is at risk. Consequently, much research has been conducted using various medical as well as computational methods in order to prevent this menace. The main aim of this paper is to review machine learning approaches to this problem and to identify the most suitable method to predict the spread of this disease for distinctive geographical areas of countries like Sri Lanka. We consider environmental factors such as climate and vegetation data, dengue case data along with the population of a specific geographic area for the disease outbreak predictions. Specifically, this paper consists of the following sections: (i) A brief description of the disease and the factors affecting the spread; (ii) review the pattern of the environmental and population factors affecting the spread; (iii) a review and comparison of machine learning algorithms for prediction of the spread of the disease (SVM, decision tree, neural network, and random forest).

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Author information

Authors and Affiliations

  1. Informatics Institute of Technology, Colombo, Sri Lanka

    Chamalka Seneviratne Kalansuriya & Achala Chathuranga Aponso

  2. University of Westminster, London, UK

    Artie Basukoski

Authors
  1. Chamalka Seneviratne Kalansuriya
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  2. Achala Chathuranga Aponso
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  3. Artie Basukoski
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Corresponding author

Correspondence to Chamalka Seneviratne Kalansuriya .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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Kalansuriya, C.S., Aponso, A.C., Basukoski, A. (2020). Machine Learning-Based Approaches for Location Based Dengue Prediction: Review. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_29

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  • DOI: https://doi.org/10.1007/978-981-15-0637-6_29

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