使用机器学习的非实验室风险因素查高血压:印尼的一项回顾性横截面研究
Reza Ishak Estiko1, Bambang Widyantoro2, Dafsah Arifa Juzar2
1Prof. Dr. Margono Soekarjo General Hospital, Purwokerto, Central Java, Indonesia.
BMJ open
|August 27, 2025
概括
机器学习模型使用11个非实验室风险因素有效选印尼人口. XGBoost模型表现出色,确定家庭史和年龄是关键预测因素.
科学领域:
- 公共卫生
- 生物统计学
- 机器学习
背景情况:
- 高血压是全球的主要健康问题,需要有效的查方法.
- 传统的查往往依赖于实验室检测,而这些检测可能不适用于所有初级保健机构.
研究的目的:
- 通过非实验室风险因素开发和验证用于高血压查的机器学习 (ML) 模型.
- 将不同ML算法的性能与物流回归基准进行比较.
主要方法:
- 一项回顾性横截面研究分析了268,210名印尼成年人 (15岁以上) 的数据.
- 使用了三个ML算法 (随机森林,梯度提升,XGBoost),以后勤回归为基准.
- 使用内部 (10倍交叉验证) 和外部验证数据集来评估查性能 (灵敏度,AUC).
主要成果:
- 在外部验证中,XGBoost模型获得了最高的性能,灵敏度为0.97和AUC为0.75.
- 确定的主要危险因素是家族高血压史 (FH-HTN),年龄,腰围和体重指数 (BMI).
- 与分类变量相比,使用年龄,WC和BMI的连续变量提高了区分能力.
结论:
- 使用现有非实验室风险因素的ML模型为高血压查提供了优质的替代方案.
- 高血压家族史和年龄是最重要的高血压预测因素.
- 这项研究强调了ML在公共卫生应用中管理大型数据集的实用性.
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