使用基于权重的监督学习系统检测心律失常的COVID-19监督学习系统
Yashodhan Ketkar1, Sushopti Gawade2
1Department of Information Technology Engineering, Pillai College of Engineering, Panvel, Maharashtra 410206, India.
概括
本研究引入了一种使用监督学习的自动化方法,用于检测COVID-19患者ECG信号中的心血管问题. 该系统在识别心律失常方面达到高准确度 (97%),有助于疾病预后.
科学领域:
- 心脏病学 心脏病学
- 传染性疾病 传染性疾病
- 人工智能在医学中的应用
背景情况:
- COVID-19 感染可能导致严重的心血管并发症,导致死亡.
- 心血管问题是COVID-19患者的主要死亡原因,也是关键预后指标.
- 从心电图 (ECG) 信号中检测心律失常等心脏异常对于评估心血管健康至关重要.
研究的目的:
- 开发一种自动化系统,用于识别COVID-19患者ECG信号中的心血管异常.
- 根据用户定义的要求,选择最合适的监督学习模型来准确检测心律失常.
- 在COVID-19的背景下,提高心血管疾病检测的效率和准确性.
主要方法:
- 利用监督学习算法来分析心电图信号.
- 开发了一个模型选择系统,根据用户要求分配权重,以确定最佳预测模型.
- 训练并测试各种模型以识别心电图波异常,表明心血管问题.
主要成果:
- 自动化系统成功地发现了心电图波中的异常.
- 选择的模型符合用户定义的要求,表现出高性能.
- 在预测任务中达到高达97%的准确性和97%的精度,用于检测心律失常.
结论:
- 拟议的自动化方法有效地检测COVID-19患者ECG信号的心血管异常.
- 模型选择系统确保部署符合特定需求的高性能模型.
- 这种方法为早期检测和改善COVID-19相关心血管并发症的预后提供了一个有希望的工具.
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