基于生活方式问卷的心血管和脑血管疾病的预测和因果推断
Riku Nambo1, Shigehiro Karashima2, Ren Mizoguchi3
1School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan.
Scientific reports
|May 7, 2024
概括
一种新的诊断模型有效地识别出患有心血管和脑血管疾病 (CCVD) 高风险的患者,包括缺血性心脏病 (IHD) 和中风. 这种工具有助于早期检测和有效分配预防性护理的资源.
科学领域:
- 心脏病学和神经病学
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 心血管和脑血管疾病 (CCVD) 是日本主要的死亡原因.
- 有效的预防措施,早期诊断和治疗对于减轻CCVD影响至关重要.
- 使用特定的健康检查数据开发了一个诊断模型,以识别患有缺血性心脏病 (IHD),中风或两者的患者.
研究的目的:
- 开发和验证一种诊断模型,用于识别患有心血管疾病,中风或CCVD风险的患者.
- 用因果推理方法分析影响CCVD发展的生活习惯.
- 改善早期检测和资源分配用于CCVD查.
主要方法:
- 利用了来自日本金沢的473734名 (≥40岁) 个体的健康检查数据 (2009-2018).
- 采用了四种机器学习算法:随机森林,物流回归,光梯度增强机器和XGBoost.
- 应用了五种因果推理方法来分析生活方式因素及其与CCVD的关系.
主要成果:
- XGBoost模型表现出卓越的性能,在中风 (0.770),IHD (0.758) 和CCVD (0.845) 中具有高AUC值.
- 因果推断确定了减少和体重增加之间的联系.
- 在CCVD发病后观察到生活方式行为变化.
结论:
- 开发的预测算法可以有效地选无症状心肌缺血和中风.
- 这种工具可以有效地识别高风险患者进行有针对性的二次检测.
- 这些发现支持改善资源分配和CCVD的二次预防策略.
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