为了解决类失衡问题,重新采样数据的影响 (IRCIP):对医学数据中分类算法之间对性能影响的评估
Koen Welvaars1, Jacobien H F Oosterhoff2, Michel P J van den Bekerom3,4
1Data Science Team, OLVG, Amsterdam, The Netherlands.
JAMIA open
|June 2, 2023
概括
重复采样医疗数据可以提高分类器的性能,但可能导致对积极预测的高估. 仔细考虑临床预测任务对于有效使用决策支持工具至关重要.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 医学数据集中的阶级不平衡对预测建模构成了挑战.
- 再抽样技术对不平衡医疗数据分类器性能的影响尚未完全理解.
研究的目的:
- 调查各种重新采样方法对不同分类算法的性能的影响.
- 评估重新采样对预测泌尿病患者30天计划外再入院的有用性.
主要方法:
- 在没有纠正的数据集上训练了多个分类算法,随机低抽样,四个比率的合成少数群体过量抽样技术 (SMOTE) 和自适应合成抽样 (ADASYN).
- 使用曲线下面面积 (AUC),精度,回忆,Brier分数和校准指标来评估性能.
- 对泌尿病患者进行了30天无计划再入院的案例研究.
主要成果:
- 对大多数算法来说,重新采样显著提高了AUC和精度.
- 在所有算法中,回忆显著改善,而Brier分数下降.
- 再抽样导致校准扭曲,高估了积极的预测.
结论:
- 重复采样技术提高了分类算法性能,但损害了模型校准.
- 尽管存在校准问题,但重新抽样可以在临床上提供实用性,特别是用于识别极端风险病例.
- 精确定义临床预测任务是指导临床决策支持再抽样应用的关键.
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