Dengue Cases Prediction Using Machine Learning Approach

Authors

  • Aima Aziz Khwaja fared University of Engineering and Technology, Pakistan
  • Azka Aziz Virtual University, Pakistan.

DOI:

https://doi.org/10.52131/jcsit.2021.0201.0007

Keywords:

Dengue Fever (DF), Machine learning, Training dataset, Prediction, forecasting and experimental data

Abstract

Dengue fever, spread by mosquitoes, affects about 3.9 billion people worldwide. Health officials could use indicators of dengue fever outbreaks to start taking preventative measures. Controlling dengue fever may be more straightforward for local authorities if they have timely and accurate disease forecasts. As one of the most rapidly spreading diseases globally, dengue fever is a threat to everyone. Dengue outbreaks can be predicted using machine learning, according to this study. Dengue prediction models could benefit from nature-based algorithms being boosted or used. The only thing that mattered in the prediction and training model was the week of the year, which was the only thing that signified. A standard machine learning algorithm cannot simulate long-term dependencies in time-series data, which is necessary for accurate projections in Dengue fever cases. When it comes to developing risk criteria for severe Dengue, machine learning could be a valuable implement in determining the possible behavior to formulate.

Author Biographies

Aima Aziz, Khwaja fared University of Engineering and Technology, Pakistan

MPhil Scholar, Computer Science and Information Technology

Azka Aziz, Virtual University, Pakistan.

MPhil Scholar, Computer Science and Information Technology

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Published

2021-12-31

How to Cite

Aziz, A. ., & Aziz, A. . (2021). Dengue Cases Prediction Using Machine Learning Approach. IRASD Journal of Computer Science and Information Technology, 2(1), 13–25. https://doi.org/10.52131/jcsit.2021.0201.0007