Feature extraction and prediction of Dengue Outbreaks

Authors

  • Kunal Parikh  Information Technology Department, A. D. Patel Institute of Technology, Karamsad, Gujarat, India
  • Tanvi Makadia  Information Technology Department, A. D. Patel Institute of Technology, Karamsad, Gujarat, India
  • Harshil Patel  Information Technology Department, A. D. Patel Institute of Technology, Karamsad, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT206544

Keywords:

Machine Learning, K-Nearest Neighbors, Prediction, Dengue, Meteorological data

Abstract

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

References

  1. Srinivasa Rao Mutheneni,Andrew P Morse,Cyril Caminade,Suryanaryana Murty Upadhyayula, “Dengue burden in India: recent trends and importance of climatic parameters”
  2. Aditya Lia Ramadona,Lutfan Lazuardi,Yien Ling Hii,Åsa Holmner,Hari Kusnanto,Joacim Rocklöv, “Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data”
  3. Sadegh Bafandeh Imandoust, Mohammad Bolandraftar, “Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background ”
  4. Jason Brownlee, “K-Nearest Neighbors for Machine Learning”
  5. Raghvendra Jain, Sra Sontisirikit, Helmut Prenginger, “Prediction of dengue outbreaks based on disease surveillance, meteorological and socioeconomic data”
  6. Appavu, MohamedMallick, G.Chinthana, “Improved prediction of dengue outbreak using combinatorial feature selector and classifier based on entropy weighted score based optimal ranking”
  7. Wei-Chun Tseng, Chi-Chung Chen, Ching-Cheng Chang, Yu-Hsien Chu “Estimating the economic impacts of climate change on infectious diseases: a case study on dengue fever in Taiwan”
  8. Siriyasatien, S. Chadsuthi, K. Jampachaisri, K. Kesorn “Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science Processes”
  9. J E T Akinsola “Supervised Machine Learning Algorithms: Classification and Comparison”
  10. Ayush Kumar Rathor, Dr.RanjanaRajnish “Comprehensive Review of Data Visualization Techniques using Python”

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-10-30

Issue

Volume 6, Issue 5, September-October-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kunal Parikh, Tanvi Makadia, Harshil Patel, " Feature extraction and prediction of Dengue Outbreaks" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 5, pp.216-222, September-October-2020. Available at doi : https://doi.org/10.32628/CSEIT206544