论文标题
一项关于班级不平衡学习在免疫后对不良事件严重性预测的应用的比较研究
A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization
论文作者
论文摘要
我们与中国的借助卫生委员会合作，我们提出了一个预测系统，以根据免疫后不良事件的数据来预测患有不良反应的儿童的住院。我们从数据中提取了多个功能，并选择“住院或不作为分类目标”。由于数据是不平衡的，因此我们使用了各种班级不平衡学习方法来培训并改善了Rusboost算法。实验结果表明，在这些算法中，ROC曲线在ROC曲线下的最高面积是最高的。此外，我们将这些平衡的学习方法与一些常见的机器学习算法进行了比较。我们将改进的Rusboost与动态Web资源开发技术相结合，以构建一个评估系统，并为相关医生提供信息输入和疫苗接种响应预测能力。
In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classification. Since the data are imbalanced, we used various class-imbalance learning methods for training and improved the RUSBoost algorithm. Experimental results show that the improved RUSBoost has the highest Area Under the ROC Curve on the target among these algorithms. Additionally, we compared these class-imbalance learning methods with some common machine learning algorithms. We combined the improved RUSBoost with dynamic web resource development techniques to build an evaluation system with information entry and vaccination response prediction capabilities for relevant medical practitioners.
