Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT206544 216 Feature extraction and prediction of Dengue Outbreaks Kunal Parikh 1 , Tanvi Makadia 2 , Harshil Patel 3 Information Technology Department, A. D. Patel Institute of Technology, Karamsad, Gujarat, India Article Info Volume 6, Issue 5 Page Number: 216-222 Publication Issue : September-October-2020 Article History Accepted : 01 Oct 2020 Published : 14 Oct 2020 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. Keywords: Machine Learning, K-Nearest Neighbors, Prediction, Dengue, Meteorological data I. INTRODUCTION Dengue is a viral fever transmitted by female mosquitoes. We currently have no vaccine or specific course of treatment for dengue. Dengue outbreaks have been rapidly increasing, the number of dengue cases reported to WHO increased over 8 fold over the last two decades, from 505,430 cases in 2000 to over 2.4 million in 2010, and 4.2 million in 2019. To decrease the impact of such outbreaks and be equipped to deal with it effectively we can use modern technology. Researches indicate a strong relation between dengue outbreaks and meteorological data. Through this paper, we want to throw insight into how we can use Machine learning (KNN model) to predict the potential dengue outbreak so that it can be contained at the rudimentary stage. The dengue virus (DEN) belongs to the family Flaviviridae and genus Flavivirus. It consists of four distinct serotypes (DEN-1, DEN-2, DEN-3 and DEN- 4) which are closely related to each other. The Aedes aegypti mosquito is the primary vector that sends the infections that cause dengue. The infections are transmitted to people through the bite of an infective female Aedes mosquito, which predominantly passes the infection while feeding on the blood of an infected individual. Earlier, dengue was limited to few southern states of India, however, from 2001 the total number of dengue cases has significantly increased and has been widely spread across India. Initially, dengue was spread in urban areas only, but now it has proliferated to rural areas as well. The expansion of dengue is mainly because of environmental changes, immunological factors of community, rapid unplanned urbanization