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Prediction of Dengue Fever Outbreaks using Machine Learning Methods

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Ponnada Akhil, A. Ajaya Kumar

Ponnada Akhil and Ajaya A Kumar. Prediction of Dengue Fever Outbreaks using Machine Learning Methods. International Journal of Computer Applications 183(46):52-56, January 2022. BibTeX

	author = {Ponnada Akhil and A. Ajaya Kumar},
	title = {Prediction of Dengue Fever Outbreaks using Machine Learning Methods},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {46},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {52-56},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2022921867},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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.


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