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This paper introduces a novel approach for dengue fever classification basedon online learning paradigms. The proposed approach is suitable for practicalimplementation as it enables learning using only a few training samples. Withtime, the proposed approach is capable of learning incrementally from the datacollected without need for retraining the model or redeployment of theprediction engine. Additionally, we also provide a comprehensive evaluation ofmachine learning methods for prediction of dengue fever. The input to theproposed pipeline comprises of recorded patient symptoms and diagnosticinvestigations. Offline classifier models have been employed to obtain baselinescores to establish that the feature set is optimal for classification ofdengue. The primary benefit of the online detection model presented in thepaper is that it has been established to effectively identify patients withhigh likelihood of dengue disease, and experiments on scalability in terms ofnumber of training and test samples validate the use of the proposed model.

An Online Learning Approach for Dengue Fever Classification