一种机器学习方法来分类纽约心脏协会 (NYHA) 的心力衰竭
Krystian Jandy1, Pawel Weichbroth2
1Gdansk University of Technology, Gdańsk, Poland.
Scientific reports
|May 20, 2024
概括
机器学习模型使用纽约心脏协会 (NYHA) 功能分类系统准确地对心力衰竭患者进行分类. 投票分类器实现了99.54%的准确性,为临床实践提供了一个公正的工具.
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:
- 心力衰竭是一个日益严重的全球健康危机,患者分类对治疗至关重要.
- 纽约心脏协会 (NYHA) 功能分类被广泛使用,但依赖于主观的医生评估,引入潜在的偏见.
- 开发客观工具对于提高心力衰竭患者分层的准确性和可靠性至关重要.
研究的目的:
- 开发和评估机器学习模型,以使用NYHA分类来公正评估心力衰竭严重程度.
- 为了比较决策树,随机森林和投票分类模型在分层心力衰竭患者中的表现.
- 评估机器学习作为一种补充工具的潜力,以减少临床实践中的偏见.
主要方法:
- 用434名心力衰竭患者的数据集进行模型培训和评估.
- 使用监督学习来训练决策树模型.
- 使用包括投票分类器和随机森林在内的集体学习技术来提高预测准确性.
- 模型性能被严格评估,使用10倍交叉验证与分层.
主要成果:
- 投票分类器达到最高准确率99.54%,随机森林达到96.77%,决策树达到76.28%.
- 随机森林和投票分类器都在对NYHAII类患者进行分类时表现出完美的准确性 (100%).
- 投票分类器在NYHA类I (98.7%) 和III (100%) 中显示出高准确度,而随机森林在NYHA类IV中实现了完美的准确度.
结论:
- 机器学习模型,特别是投票分类器和随机森林,在根据NYHA功能状态准确客观地分类心力衰竭患者方面显示出重大前景.
- 这些模型可以作为有价值的,公正的工具来支持临床医生,潜在地减少诊断偏见和改善患者管理.
- 进一步的研究应该探索额外的变量和数据集,以完善这些模型,并深入了解影响心力衰竭进展的因素.
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