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一种集成的机器学习方法来预测充血性心力衰竭

M Sheetal Singh¹, Khelchandra Thongam¹, Prakash Choudhary²

¹Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India.

Diagnostics (Basel, Switzerland)

|April 13, 2024

概括

早期发现充血性心力衰竭 (CHF) 是至关重要的. 这项研究使用机器学习,特别是深度神经网络,准确预测CHF,可能降低医疗保健成本并改善患者的治疗结果.

科学领域:

心脏病学心脏病学
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

充血性心力衰竭 (CHF) 是一个重要的全球健康问题,影响全球超过2600万人.
慢性心血管疾病的患病率正在增加,这凸显了有效的早期检测和诊断方法的需要.
通过先进的预测技术,可以降低当前的诊断成本.

研究的目的:

通过机器学习增强结合性心力衰竭 (CHF) 的早期诊断.
通过使用最低一组预测特征来降低CHF诊断的成本.
为了比较深度神经网络 (DNN) 与其他机器学习分类器的性能,用于CHF预测.

主要方法:

利用心血管健康研究 (CHS) 数据集进行培训和评估.
实施了一种新的预处理技术,集成C4.5用于特征选择/异常值删除和K-最近邻居 (KNN) 缺失数据归算.
使用七个统计指标,将一个深度神经网络 (DNN) 分类器与六个传统机器学习算法 (KNN,LR,NB,RF,SVM,DT) 进行了比较.

主要成果:

拟议的综合方法,特别是DNN,在CHF预测方面表现优异,与其他ML算法相比.
实现了高性能指标:97.03%的F1分数,95.30%的准确性,96.49%的灵敏度和97.58%的精度.
该研究的方法有效地减少了所需的医疗检查数量,这表明患者可能节省成本.

结论:

开发的机器学习模型,特别是DNN,为准确和经济高效的充血性心力衰竭的早期预测提供了一个有前途的工具.
综合预处理技术提高了心血管健康研究的数据质量和模型性能.
通过先进的人工智能早期预测CHF可以显著降低与该疾病相关的死亡率和发病率.

关键词:

C4.5 这样就好了.预测CHF的预测在CHS中,CHS是最重要的.DNN DNN 在线在 KNN KNN 标签上.归算是指指责一个人.

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