在中国西北部沙漠地区的人口中,两年高血压发病风险预测:前性队列研究
Yinlin Cheng1, Kuiying Gu2, Weidong Ji1
1Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Journal of medical Internet research
|March 12, 2025
概括
一个新的机器学习模型准确地预测了中国2年的高血压风险.
科学领域:
- 心血管疾病流行病学
- 医疗保健中的机器学习
- 公共卫生信息学 公共卫生信息学
背景情况:
- 高血压是一个重要的全球健康问题,也是心血管疾病的主要风险因素.
- 中国高血压的高患病率,特别是靠近沙漠地区,需要有效的早期检测和干预.
- 沙漠地区独特的环境和生活方式因素加剧了高血压负担.
研究的目的:
- 开发,校准和前性验证2年高血压风险预测模型.
- 利用来自塔克拉马坎沙漠附近人口的大规模健康检查数据.
- 在这个特定的人口群体中确定高血压风险的关键预测因素.
主要方法:
- 对1,038,170名成年人 (2019-2021) 的回顾性分析和对961,519名成年人 (2021-2023) 的前性验证.
- 使用LightGBM-RFE和LASSO进行特征选择,确定了24个关键预测因素.
- 训练并比较多个机器学习和深度学习模型,包括CatBoost,与贝叶斯式超参数优化.
主要成果:
- 在两年内,高血压发生率为15.20% (追溯) 和10.50% (前性).
- 在CatBoost模型中,AUC达到0.888 (追溯) 和0.803 (前).
- 校准模型将参与者分为风险类别,非常高风险组的发病率为41.08%;年龄,BMI和社会经济因素是关键预测因素.
结论:
- 开发的机器学习模型有效预测2年高血压风险,适用于高风险中国人群的预防护理.
- 该模型显示出出色的预测性能和临床应用的潜力.
- 一个基于网络的应用程序提高了临床和公共卫生用途的可访问性,有助于预防高血压.
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