# An Online Learning Approach for Dengue Fever Classification

## TL;DR

This paper presents an online learning method for dengue fever classification that adapts incrementally with new data, reducing the need for retraining and enabling practical, scalable diagnosis based on patient symptoms and diagnostics.

## Contribution

The paper introduces a novel online learning approach for dengue classification that learns incrementally from limited data without retraining, improving scalability and practicality.

## Key findings

- Effective identification of high-likelihood dengue patients
- Incremental learning without retraining enhances scalability
- Optimal feature set validated for dengue prediction

## Abstract

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.