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使用自动机器学习来检测美国成年人未被诊断的糖尿病:开发和验证研究

Jianxiu Liu^1,2, Fred Ssewamala^3,4, Ruopeng An^3,5

¹Division of Sports Science and Physical Education, Tsinghua University, Beijing, China.

|October 16, 2025

概括

自动机器学习 (AutoML) 模型可以有效地使用自我报告的数据在美国成年人中识别未诊断的糖尿病. 这种方法对大规模的糖尿病查和早期干预工作具有前景.

科学领域:

医疗信息学医疗信息学
公共卫生公共卫生
医疗保健中的机器学习

背景情况:

早期诊断糖尿病对于减缓疾病进展的干预措施至关重要.
很大一部分糖尿病患者仍未被诊断出来,这阻碍了及时治疗.

研究的目的:

探索自动机器学习 (AutoML) 模型在美国成年人中检测未被诊断的糖尿病的实用性.
用自报数据和生化标记来评估AutoML模型的性能.

主要方法:

利用国家健康和营养检查调查 (1999-2020年) 的数据从11,815名参与者.
使用H2O AutoML框架进行自动化模型训练和超参数调整.
与传统机器学习模型 (逻辑回归,SVM,随机森林,XGBoost) 进行AutoML性能比较.

主要成果:

自动ML模型实现了0.909.90的接收器操作特征曲线 (AUC) 下的区域优越.
在识别未被诊断的糖尿病时,具有70.26%的灵敏度和90.46%的特异性.
达到92.61%的负预测值,表明在排除糖尿病方面具有强大的可靠性.

结论:

这项研究是首次将AutoML应用于美国成年人未被诊断的糖尿病检测.
自动ML模型为一般人口的糖尿病查提供了一个强大而可扩展的工具.
这些发现支持将先进的机器学习纳入糖尿病管理中的积极公共卫生倡议.

关键词:

在AutoML中使用AutoML.机器学习是机器学习.查检查查检查查检查查检查自我报告自我报告没有确诊的糖尿病.

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