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In collaboration with the Liaoning CDC, China, we propose a prediction systemto predict the subsequent hospitalization of children with adverse reactionsbased on data on adverse events following immunization. We extracted multiplefeatures from the data, and selected "hospitalization or not" as the target forclassification. Since the data are imbalanced, we used various class-imbalancelearning methods for training and improved the RUSBoost algorithm. Experimentalresults show that the improved RUSBoost has the highest Area Under the ROCCurve on the target among these algorithms. Additionally, we compared theseclass-imbalance learning methods with some common machine learning algorithms.We combined the improved RUSBoost with dynamic web resource developmenttechniques to build an evaluation system with information entry and vaccinationresponse prediction capabilities for relevant medical practitioners.

A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization