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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 Liu^1,2, Song Dong¹, Hua Yang³

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

The Journal of international medical research

|June 13, 2024

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.

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.

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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