An Online Learning Approach for Dengue Fever Classification
This addresses dengue fever diagnosis for healthcare applications, but it is incremental as it applies online learning to a specific domain.
The paper tackles dengue fever classification by introducing an online learning approach that learns incrementally from few training samples without retraining, and it effectively identifies high-likelihood patients with validated scalability in experiments.
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.