An efficient method to predict dengue outbreaks in kuala lumpur

In recent years, there has been a surge in dengue outbreaks in Malaysia. A dengue outbreak can cause severe damages to the society. Hence, it is critical to be able to predict a dengue outbreak in advance to minimize the damage and loss. In this paper, we propose a new machine learning approach to predict the number of dengue cases in Kuala Lumpur, in particular the areas surrounding the University of Malaya (UM) Medical Centre. We identified several different factors that can contribute to a surge in the number of dengue cases that occurred near the UM Medical Centre. Apart from the daily mean temperature and daily rainfall factors that have been frequently used in other studies, we also considered the enhanced vegetation index (EVI) as an input factor to our prediction engine. We trained our linear regression model on these three factors against the number of dengue cases from 2001 to 2010. We then tested our model on the 2011 data. The experimental results showed that our approach was able to predict the number of dengue cases 16 days in advance with high accuracy.