  This paper introduces a novel approach for dengue fever classification based
on online learning paradigms. The proposed approach is suitable for practical
implementation as it enables learning using only a few training samples. With
time, the proposed approach is capable of learning incrementally from the data
collected without need for retraining the model or redeployment of the
prediction engine. Additionally, we also provide a comprehensive evaluation of
machine learning methods for prediction of dengue fever. The input to the
proposed pipeline comprises of recorded patient symptoms and diagnostic
investigations. Offline classifier models have been employed to obtain baseline
scores to establish that the feature set is optimal for classification of
dengue. The primary benefit of the online detection model presented in the
paper is that it has been established to effectively identify patients with
high likelihood of dengue disease, and experiments on scalability in terms of
number of training and test samples validate the use of the proposed model.
