机器学习模型用于预测心血管疾病中 carotid 斑块的危险因素
Chengling Bin1, Qin Li1, Jing Tang1
1Health Management Section, The First People's Hospital of Neijiang, Neijiang, China.
Frontiers in cardiovascular medicine
|October 9, 2023
概括
机器学习有效地预测了动脉斑块风险,这是心血管疾病 (CVD) 的关键指标. XGBoost表现最好,将年龄,吸烟,酒精和BMI确定为早期心血管疾病预防的重要风险因素.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 心血管疾病 (CVD) 是一个主要的全球健康问题.
- 冠状动脉斑块是心血管疾病的重要危险因素,表明动脉样硬化严重程度.
- 在心血管疾病的预防和管理中,早期预测带斑块至关重要.
研究的目的:
- 开发和比较机器学习算法,用于预测动脉斑块风险.
- 为了确定动脉斑块形成的关键预测因素.
- 评估在常规CVD管理中使用机器学习的可行性.
主要方法:
- 在4659名患者的体检数据上训练和验证了8个机器学习算法.
- 用10倍的交叉验证来进行模型优化.
- 沙普利添加式解释 (SHAP) 用于特征重要性分析.
- 用AUC,精度和特异性来评估模型性能.
主要成果:
- XGBoost算法表现出卓越的性能,AUC为0.808,准确度为0.749,特异性为0.762.
- 鉴定出动脉斑块形成的关键预测因素包括年龄,吸烟,饮酒以及身体质量指数 (BMI).
- 机器学习模型是可行的预测动脉斑块风险.
结论:
- 开发的机器学习模型可以整合到慢性疾病管理中.
- 这使得能够主动查心动脉斑块,从而有可能改善心血管疾病患者的治疗结果.
- 机器学习为早期心血管疾病风险评估和预防提供了有价值的工具.
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