机器学习分类器的比较,用于预测卢旺达心力衰竭患者的医院再接收
Theogene Rizinde1, Innocent Ngaruye2, Nathan D Cahill3
1College of Business and Economics, University of Rwanda, Kigali 4285, Rwanda.
Journal of personalized medicine
|September 28, 2023
概括
机器学习模型可以预测心力衰竭 (HF) 医院再入院. 随机森林 (RF) 分类器表现出最高的准确性,为改善卢旺达患者管理和降低医疗保健成本提供了潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生 公共卫生
背景情况:
- 由于心力衰竭 (HF) 的高再入院率构成了全球重大公共卫生挑战.
- 有效的HF管理需要识别高风险患者进行早期干预.
- 卢旺达面临与治疗心力衰竭相关的大量成本.
研究的目的:
- 评估和比较六种机器学习模型在预测心力衰竭患者20天住院再入院风险方面的表现.
- 确定最有效的机器学习模型来预测卢旺达背景下的高频再入院.
主要方法:
- 六个机器学习模型的比较:多层感知子 (MLP),K-近邻 (KNN),逻辑回归 (LR),决策树 (DT),随机森林 (RF) 和支持向量机器 (SVM).
- 使用诸如AUC (接收器操作特征曲线下的面积),灵敏度和特异性等指标的模型性能评估.
- 应用模型来预测心力衰竭患者退院后再入院风险.
主要成果:
- 随机森林 (RF) 分类器实现了最高的性能,AUC为94%.
- 支持向量机 (SVM),多层感知子 (MLP) 和K-近邻 (KNN) 模型显示可比的AUC为88%.
- 决策树 (DT) 的表现明显较低,AUC为57%.
结论:
- 随机森林 (RF) 模型对于预测心力衰竭医院再入院非常有效.
- 卢旺达的医院可以利用射频分类器来识别高风险的HF患者,从而实现及时干预.
- 实施这种预测模型可能会改善患者的治疗结果,并减少与HF再入院相关的医疗负担.
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