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一项基于机器学习的关于老年高血压患者心脏病发病风险的预测研究

Fei Si¹, Qian Liu¹, Jing Yu²

¹Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.

|January 11, 2025

概括

机器学习模型可以预测老年高血压患者的心脏病风险. 伴随性疾病,如失脂症,增加风险,有助于早期识别.

科学领域:

心脏病学心脏病学
老年病的医生老年病的医生
医疗信息学医疗信息学

背景情况:

心脏病仍然是老年人死亡的主要原因.
高血压是老年人心血管事件的重要危险因素.
早期风险分层对于老年人心血管健康的及时干预至关重要.

研究的目的:

开发和验证机器学习模型,用于预测老年高血压患者心脏病的发生.
在这个人口群体中确定与心脏病发展相关的关键风险因素.
加强心血管疾病早期风险识别策略,在患有高血压的老年人中.

主要方法:

利用了来自中国健康与退休长度研究 (CHARLS) 的934名参与者 (60岁以上) 的数据,并对其进行了7年的随访 (2011-2018).
采用机器学习算法,包括物流回归,XGBoost和深度神经网络 (DNN) 来构建预测模型.
使用歧视 (ROC-AUC),校准和临床决策曲线评估模型性能.

主要成果:

26.03%的老年高血压患者在7年内患上心脏病.
伴随性疾病,如失脂症,慢性肺部疾病和关节炎/风湿性疾病与更高的心脏病风险有关.
XGBoost实现了最高的ROC-AUC (0.67),而后勤回归显示了最佳的校准. 功能选择改善了模型性能.

结论:

机器学习模型使用查尔斯数据有效预测老年高血压患者的心脏病风险.
识别患有特定并发症的患者可以促进早期风险评估.
这些发现支持开发有针对性的干预措施,以预防心脏病在这个弱势群体.

关键词:

心脏病心脏病心脏病在高血压的高血压.机器学习机器学习年龄较大的患者.风险预测风险预测

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