机器学习技术用于使用综合医疗保健数据更好地预测心血管疾病
1Department of Computer Engineering, Faculty of Engineering, Balıkesir University, Balıkesir, Türkiye.
Frontiers in artificial intelligence
|December 25, 2025
概括
机器学习模型可以使用患者数据改善心血管疾病 (CVD) 检测. CatBoost模型表现出高准确度,达到94.1%的AUC,可进行强大的心血管疾病预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 心血管健康 心血管健康
背景情况:
- 心血管疾病 (CVD) 构成了全球严重的健康负担.
- 早期发现心血管疾病对于有效管理和改善患者治疗结果至关重要.
- 越来越需要先进,可靠的诊断工具来补充传统方法.
研究的目的:
- 探索机器学习 (ML) 算法的潜力,以准确诊断心血管疾病.
- 开发和评估ML模型使用集成的,大规模的医疗保健数据集.
- 确定最有效的ML模型来预测心血管疾病风险.
主要方法:
- 从多个数据库中收集和整合了广泛的,公开可用的医疗保健记录.
- 执行数据预处理,包括清理,特征调整和处理缺失值.
- 在311,710个样本的数据集上训练和评估了几种机器学习模型.
主要成果:
- CatBoost机器学习模型在预测心血管疾病方面取得了最高的表现.
- CatBoost模型得到的曲线下的面积 (AUC) 为94.1%.
- 这表明模型的诊断预测具有很高的准确性和稳定性.
结论:
- 机器学习决策支持系统在心血管疾病诊断方面显示出重大前景.
- CatBoost 模型为早期 CVD 检测提供了强大而准确的方法.
- 使用ML利用大型医疗数据集可以提高诊断精度和患者护理.
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