Author Description

Dengue is one of the common infectious diseases which is caused by the dengue virus and transmitted to humans by mosquitoes with this many are infected in varied regions around the world per year. The reason for this virus is atmospheric conditions, which play a vital role in the outbreak of dengue. Therefore early prediction of dengue is the key to regulate outbreaks and reduces the transmission within the community. To overcome this we are using various machine learning (ML) algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest tree (RF), and Decision Tree (DT) are used to predict the dengue outbreak. Prediction is done based on weather parameters like monthly wise maximum temperature, minimum temperature, average temperature, mean temperature, humidity, and Precipitation which is considered as weather dataset and this weather dataset is pre-processed using label encoding function before applying into the training models. The performances of all the models are calculated based on weather datasets.