Balancing of an imbalanced dataset by applying SMOTE variants and predicting neonatal mortality using ensemble learning techniques

Sivarajan A, Bala Aditya A, Sivasankar E
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Abstract

Dynamic environment and imbalanced datasets are unavoidable challenges in developing medical diagnostic tools where incremental learning is a necessity. The prediction tools upon imbalanced data normally work with majority class bias, and it is not easy to recognize faulty classes. This work aims to solve the class imbalance problem by generating synthetic data using SMOTE variants to balance the dataset and predict the neonatal mortality by adopting different ensemble classification methods. This system will be applied to diagnose newborns, vulnerable to die in the initial period of 28 days after birth.
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动态环境和不平衡的数据集是开发医疗诊断工具中不可避免的挑战,而增量学习是必要的。基于不平衡数据的预测工具通常具有多数类偏差,并且不容易识别错误类。本工作旨在通过使用SMOTE变量生成合成数据来平衡数据集,并通过采用不同的集成分类方法来预测新生儿死亡率,从而解决类失衡问题。该系统将用于诊断出生后28天内易死亡的新生儿。
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