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

TL;DR

This paper evaluates different class-imbalance learning methods, especially an improved RUSBoost algorithm, for predicting hospitalization risk in children after immunization, demonstrating superior performance and practical deployment in a medical decision support system.

Contribution

It introduces an improved RUSBoost algorithm tailored for adverse event severity prediction and develops a web-based evaluation system for medical practitioners.

Findings

Improved RUSBoost achieved the highest AUC among tested algorithms.

Class-imbalance methods outperformed standard machine learning algorithms.

The system supports real-time vaccination response prediction.

Abstract

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.