基于机器学习的模型可以预测正常血压在5年后期内转化为高血压
Aref Andishgar1, Sina Bazmi2, Reza Tabrizi3
1USERN Office, Fasa University of Medical Sciences, Fasa, Iran.
PloS one
|March 14, 2024
概括
机器学习模型可以预测中东农村人口的高血压风险. 关键预测因素包括静缩血压,性别和腰比,有助于早期干预策略.
科学领域:
- 心血管健康 心血管健康
- 公共卫生 公共卫生
- 机器学习在医学中的应用
背景情况:
- 高血压的患病率在全球范围内有所不同,因此需要对特定地区的风险进行预测.
- 了解高血压发展因素对于有针对性的干预至关重要.
研究的目的:
- 预测5年内患高血压风险的个体.
- 确定中东农村人口中新发高血压的关键预测因素.
主要方法:
- 使用来自伊朗农村的Fasa成年人队列研究 (FACS) 数据进行的纵向研究.
- 采用了10个受监督的机器学习算法,包括光梯度增强机 (LGBM).
- 使用特征缩放,一次性编码,用于数据平衡的SMOTE和用于特征选择的SHAP.
主要成果:
- 在2,288名参与者中,10.9%的人患上了新的高血压.
- 最优的LGBM模型实现了0.67.7的AUC.
- 确定了顶级预测因素:基线静缩血压,性别,腰部和部的比率,出血症和家族病史.
结论:
- 机器学习模型对识别高血压风险的个体显示出有前途.
- 这些模型可以支持早期的生活方式或医疗干预.
- 预测工具可以增强对高血压预防的决策.
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