从少量的健康检查数据中预测糖尿病的数据协作分析
Go Uchitachimoto1, Noriyoshi Sukegawa2, Masayuki Kojima1
1Master's Program in Service Engineering, University of Tsukuba, Tsukuba, Japan.
Scientific reports
|July 21, 2023
概括
数据协作 (DC) 分析通过使用逻辑回归 (LR) 来提高从小数据集的糖尿病预测,提高准确性. 在这项研究中,渐变增强决策树 (GBDT) 的性能在DC分析下降.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 像渐变增强决策树 (GBDT) 这样的机器学习模型在使用大数据集的糖尿病预测中表现出高准确性.
- 从有限的数据来准确预测糖尿病的挑战仍然存在.
研究的目的:
- 通过使用数据协作 (DC) 分析,研究从小型数据集中准确预测糖尿病的可行性.
- 为了比较物流回归 (LR) 和GBDT的性能,使用和不使用DC分析.
主要方法:
- 利用了1502名公民的健康检查数据和1399名患者的健康史数据.
- 应用DC分析以整合和分析来自两个机构的数据,同时确保保密性.
- 使用ROC-AUC和回忆指标评估了LR和GBDT的性能.
主要成果:
- 电流分析改善了LR的性能 (ROC-AUC:0.858至0.875,回忆:0.970至0.993).
- 由于与数据共享方法的兼容性问题,DC分析导致GBDT性能下降.
- 即使使用有限的数据 (324名公民),DC分析也显著改善了LR的性能 (ROC-AUC: +11%,回顾: +12%).
结论:
- 电流分析能够使用逻辑回归从小型数据集准确预测糖尿病.
- 梯度增强决策树 (GBDT) 没有从使用经过测试的保密数据共享方法进行DC分析中受益.
- 通过数据协作,物流回归显示出糖尿病预测在资源有限的环境中具有前景.
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