一种用于登革热分类的在线学习方法
An Online Learning Approach for Dengue Fever Classification
摘要：
介绍了一种基于在线学习范式的登革热分类新方法。所提出的方法是适合于实际的实施，因为它使学习只使用少量的训练样本。随着时间的推移，所提出的方法能够从收集的数据中增量学习，而不需要重新训练模型或重新部署预测引擎。此外，我们还提供了一个全面的评估机器学习方法预测登革热。对拟议管道的输入包括记录的患者症状和诊断调查。离线分类器模型已被用于获得基线分数，以确定特征集对于登革热的分类是最佳的。本文中提出的在线检测模型的主要好处是，它已经建立，以有效地识别登革热疾病的可能性高的患者，并在训练和测试样本的数量方面的可扩展性实验验证了所提出的模型的使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
