改善冠心病诊断:使用临床医疗记录对数据预处理技术和机器学习模型进行比较分析
Chun-Wei Tseng1,2, Ling-Chun Sun2, Ke-Feng Lin3,4
1Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Health informatics journal
|August 6, 2025
概括
在缩放之前通过平衡预处理数据,显著提高了用于诊断冠心病的机器学习准确性. XGBoost表现出强大的性能,突出了AI.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 心血管疾病研究研究
背景情况:
- 机器学习 (ML) 在早期冠心病 (CHD) 诊断方面表现有前途.
- 优化数据预处理对于临床环境中的ML模型性能至关重要.
研究的目的:
- 评估不同数据预处理序列对ML对CHD的预测精度的影响.
- 为了比较在平衡之前的标准化与在扩展之前的平衡.
主要方法:
- 利用了两个临床数据集:心力衰竭患者和克利夫兰心脏病数据集.
- 对比了两个预处理序列:标准化-平衡与平衡-缩放.
- 训练了六个ML模型 (XGBoost,GBDT,AdaBoost,Random Forest,KNN,RaSE) 使用80:20的分割,通过贝叶斯优化优化超参数.
主要成果:
- 这两种预处理序列在克利夫兰数据集上都取得了完美的准确性.
- 对于心力衰竭数据集,在缩放之前进行平衡,比在平衡之前进行标准化 (93.33%) 获得更高的准确性 (95%).
- 在缩放之前的平衡也导致了更高的宏观平均和加权平均F1分数,XGBoost显示出一致的稳定性.
结论:
- 数据预处理的顺序极大地影响了ML模型的有效性,尤其是在不平衡的临床数据的情况下.
- 在缩放之前平衡数据可以提高CHD预测的分类准确性.
- XGBoost是潜在的临床决策支持系统的可靠模型,在数字健康领域推进AI.
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