Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification. This model is named Hellinger Distance Undersampling (HDUS). It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class. An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy.

中文翻译：

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使用Hellinger距离欠采样模型改进不平衡医疗数据集中疾病类别的分类

医学数据集中的类别分布不平衡是一项具有挑战性的任务，阻碍了疾病的正确分类。当健康类实例的数量远大于疾病类实例的数量时，就会出现这种情况。为了解决这个问题，我们提出对健康类实例进行欠采样以改进疾病类分类。该模型称为 Hellinger 距离欠采样 (HDUS)。它采用海林格距离来衡量多数类实例与其相邻少数类实例之间的相似性，以有效地区分类并提高每个类的区分能力。使用三个分类器对四个不平衡的医疗数据集进行了广泛的实验，将 HDUS 与基线模型和三个最先进的欠采样模型进行比较。结果表明，HDUS 在灵敏度、F1 测量和平衡精度方面比其他模型表现更好。