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.
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.
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.
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