CFP last date
22 July 2024
Reseach Article

Prediction of Dengue Fever Outbreaks using Machine Learning Methods

by Ponnada Akhil, A. Ajaya Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 46
Year of Publication: 2022
Authors: Ponnada Akhil, A. Ajaya Kumar

Ponnada Akhil, A. Ajaya Kumar . Prediction of Dengue Fever Outbreaks using Machine Learning Methods. International Journal of Computer Applications. 183, 46 ( Jan 2022), 52-56. DOI=10.5120/ijca2022921867

@article{ 10.5120/ijca2022921867,
author = { Ponnada Akhil, A. Ajaya Kumar },
title = { Prediction of Dengue Fever Outbreaks using Machine Learning Methods },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 46 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 52-56 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921867 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:20:10.820099+05:30
%A Ponnada Akhil
%A A. Ajaya Kumar
%T Prediction of Dengue Fever Outbreaks using Machine Learning Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 46
%P 52-56
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

Mosquitoes are the major source of the spread of dengue. The blood sample of a person is mostly used for detection of dengue. But there are various other factors which are responsible for dengue prevalence.In this project,weather conditions such as dew point, humidity, minimum and maximum temperatures along with precipitation of places present in India are considered to predict whether dengue exists or not. The four supervised algorithms- k-nearest neighbors, random forest, decision tree and support vector machines are compared to predictions. The results of these algorithms are compared based on accuracy, precision, and recall.

  1. C. P. G. Management, D. Infectionadults, and T. Edition, CPG Management of Dengue Infection In Adults (Third Edition) 2015 1. 2015.
  2. Wikipedia-
  3. Rajathi, N., Brahanambika, R. and Manjubarkavi, K., 2018. Early detection of dengue using machine learning algorithms. International Journal of Pure and Applied Mathematics, [online] 118(18), pp.3881-3886. Available at: <:> [Accessed 10 November 2021].
  4. Muzakki, M. and Nhita, F., 2018. The Spreading Prediction of Dengue Hemorrhagic Fever (DHF) in Bandung Regency Using K-Means Clustering and Support Vector Machine Algorithm. 2018 6th International Conference on Information and Communication Technology (ICoICT),.
  5. binti MohdZainee, N. and Chellappan, K., 2016. A preliminary dengue fever prediction model based on vital signs and blood profile. 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES),.
  6. Anitha, A. and Wise, D., 2018. Forecasting Dengue Fever using Classification Techniques in Data Mining. 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT),.
  7. Nakvisut, A. and Phienthrakul, T., 2018. Two-Step Prediction Technique for Dengue Outbreak in Thailand. 2018 International Electrical Engineering Congress (iEECON),.
  8. Fahmi, A., Purwitasari, D., Sumpeno, S. and Purnomo, M., 2020. Performance Evaluation of Classifiers for Predicting Infection Cases of Dengue Virus Based on Clinical Diagnosis Criteria. 2020 International Electronics Symposium (IES),.
  9. Husin N A, Salim N, Ahmad A R. Modeling of dengue outbreak prediction in Malaysia: A comparison of Neural Network and Nonlinear Regression Model[C]// International Symposium on Information Technology. IEEE, 2008, 1-4
  10. Mustaffa Z, Yusof Y. A Comparison of Normalization Techiques in Predicting Dengue Outbreak[J]. International Proceedings of Economics Development & Research, 2011.
  11. Quinlan, J. R. (1986). "Induction of decision trees" . Machine Learning. 1: 81–106. doi:10.1007/BF00116251. S2CID 189902138.
  12. Ho, T.K. (1995) Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995, 278-282.
  13. Bartlett P. and Shawe-Taylor J., “Generalization performance of support vector machine and other pattern classifiers”, In C. ~Burges B. ~Scholkopf, editor, “Advances in Kernel Methods-Support Vector Learning”, pp. 43–55, MIT press, 1998.
  14. Cover, Thomas M.; Hart, Peter E. (1967). "Nearest neighbor pattern classification" (PDF). IEEE Transactions on Information Theory. 13 (1): 21–27. CiteSeerX10.
Index Terms

Computer Science
Information Sciences


Dengue Prevalence Machine Learning SVM Random Forest Decision Tree K-NN