使用常规临床变量对糖尿病患者队列中高血压预测的比较机器学习模型
1Department of Basic Medical Sciences, Taibah University, Medina, Saudi Arabia.
Archives of endocrinology and metabolism
|November 6, 2025
概括
机器学习模型可以使用常规临床数据预测糖尿病患者的高血压. 神经网络模型表现出色,有效地识别高风险个体.
科学领域:
- 生物医学信息学 生物医学信息学
- 心血管疾病研究研究
- 糖尿病管理 糖尿病管理
背景情况:
- 高血压是糖尿病患者的重要并发症.
- 预测模型可以帮助在这个人群中早期识别和管理高血压.
研究的目的:
- 为了比较各种机器学习模型在糖尿病患者中预测高血压的性能.
- 确定糖尿病患者高血压风险预测的关键临床变量.
主要方法:
- 利用了来自35,346个人的行为风险因素监测系统数据.
- 训练和评估的模型包括神经网络,梯度增强,随机森林,自适应增强和物流回归.
- 使用AUC,准确性,精度,回忆和F1得分来评估模型性能,解决类不平衡问题.
主要成果:
- 神经网络模型显示了最高的预测性能 (AUC 0.689,准确率为76.5%).
- 梯度增强模型显示了类似的结果.
- 年龄和体重指数被确定为糖尿病患者高血压的最重要的预测因素.
结论:
- 机器学习模型有望利用现有临床数据预测糖尿病患者的高血压风险.
- 神经网络方法为识别有风险的个体提供了出色的预测能力.
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