Machine learning for dengue outbreak prediction: An outlook

Abstract

Dengue is a standout amongst the most well-known viral sicknesses in people. More than 33% of the total populace of world is under risk [9], including many cities of India. Timely prediction of dengue can save person’s life by alerting them to take proper diagnosis and care. Prediction of infectious disease, such as Dengue, is a challenging task and most of the prediction methods are still in their infancy. Microarray and RNA-Seq data have been widely deployed for developing predictive model of various dengue. In this project, we propose to develop a machine learning model to predict Dengue. We will take various machine learning classifiers ranging from simple classifiers, like Decision Tree, Naive Bayes, Model Tree, to complex algorithms such as Support Vector Machines, Neural Networks, Gene Expression Programming, Genetic Programming and ensemble classifiers. The algorithm giving the highest prediction accuracy will be considered for the development of Dengue Prediction Tool. We also propose to develop a novel ensemble classifier for Dengue outbreak prediction. Keywords: Dengue fever, Machine learning algorithm, Prediction, Classification, clinical symptoms, genes