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比较四种机器学习模型在预测中国人口2型糖尿病发病的准确性:一项回顾性研究.

Hongzhou Liu1,2, Song Dong1, Hua Yang3

  • 1Department of Endocrinology, Aerospace Center Hospital, Beijing, China.

The Journal of international medical research
|June 13, 2024
PubMed
概括

机器学习模型有效地利用健康数据预测了中国的2型糖尿病 (T2DM) 风险. 后勤回归和随机森林模型确定了干预的高风险个体.

关键词:
中国人口中国人口.机器学习 机器学习在XGBoost中使用.禁食血葡萄糖 禁食的血葡萄糖逻辑回归的逻辑回归方法预测模型 预测模型随机的森林随机的森林久坐不动的时间.支持矢量机器的支持矢量机器2型糖尿病是什么?

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科学领域:

  • 计算流行病学计算流行病学
  • 生物医学信息学是生物医学信息学.
  • 公共卫生 公共卫生

背景情况:

  • 2型糖尿病 (T2DM) 是全球健康的重大负担.
  • 早期识别高风险T2DM个体对于有效的预防策略至关重要.
  • 使用电子健康记录的预测建模为风险分层提供了一个有希望的方法.

研究的目的:

  • 评估机器学习 (ML) 模型在预测5年T2DM风险方面的有效性.
  • 分析来自中国人口的年度健康检查记录.
  • 为了比较不同的ML算法对T2DM风险预测的性能.

主要方法:

  • 对46247名患者的健康检查记录进行了回顾性分析.
  • 训练和验证极端梯度增强 (XGBoost),支持向量机 (SVM),物流回归 (LR) 和随机森林 (RF) 模型.
  • 使用接收器操作特征 (ROC) 曲线和校准图表评估模型歧视.

主要成果:

  • 对T2DM风险的关键预测因素包括禁食血葡萄糖,年龄和久坐时间.
  • 物流回归 (LR) 实现的ROC曲线下的面积 (AUC) 为0.914 (培训) 和0.913 (验证).
  • 随机森林 (RF) 显示AUC为0.998 (培训) 和0.838 (验证),对低风险和高风险组进行了令人满意的校准.

结论:

  • 物流回归 (LR) 和随机森林 (RF) 模型对于预测中国人口T2DM风险是有效的.
  • 这些ML模型可以帮助识别高风险个体.
  • 早期识别有助于针对性干预,以预防T2DM并发症和残疾.