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使用人工智能改进心血管疾病预测的异步联合学习.

Muhammad Amir Khan1, Musleh Alsulami2, Muhammad Mateen Yaqoob1

  • 1Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan.

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概括

这项研究引入了用于心脏预测的异步联合深度学习方法 (AFLCP). 与传统方法相比,AFLCP提高了心脏病预测的准确性,并降低了通信成本.

关键词:
分布式机器学习 (DLM) 是一种分布式机器学习.医疗保健应用程序 医疗保健应用心脏病预测 心脏病预测机器学习是机器学习.可靠的深度模型.

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科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 预测心脏病对于医疗保健专业人员来说至关重要.
  • 深度学习 (DL) 显示了准确的心脏预测的前景.
  • 现有方法在效率和准确性方面可能面临挑战.

研究的目的:

  • 为心脏预测引入一种新的异步联合深度学习方法 (AFLCP).
  • 为了提高心脏病预测深度神经网络 (DNN) 模型的准确性和融合.
  • 为了降低心脏应用的联合学习的通信成本.

主要方法:

  • 开发了用于心脏预测的异步联合深度学习方法 (AFLCP).
  • 利用心脏病数据集和深度神经网络 (DNN).
  • 实施了异步参数更新和DNN的时间加权聚合.

主要成果:

  • 根据AFLCP的方法,比起基线方法,AFLCP的方法表现优越.
  • 在心脏预测方面,AFLCP实现了更高的模型准确性.
  • 提出的方法显示了通信成本的降低.

结论:

  • 对心脏预测 (AFLCP) 的异步联合深度学习方法是有效的.
  • AFLCP为心脏病预测提供了更高的准确性和通信效率.
  • 这种新的方法促进了深度学习在心脏病学中的应用.