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从临床参数检测心血管疾病,使用一维卷积神经网络

Mohammad Mahbubur Rahman Khan Mamun¹, Tarek Elfouly¹

¹Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA.

Bioengineering (Basel, Switzerland)

|July 29, 2023

概括

与传统方法相比,一种新的混合一维卷积神经网络 (1D CNN) 为检测心脏病提供了更高的准确性. 这种人工智能方法对早期和更可靠的心脏病诊断有希望.

科学领域:

心脏病学和人工智能的人工智能
生物医学数据分析

背景情况:

心脏病构成了重大的公共卫生挑战,需要早期检测才能有效管理.
目前的诊断方法往往不方便,昂贵,不适合频繁监测.
新兴的人工智能 (AI) 技术,包括机器学习 (ML) 和深度学习 (DL),显示出改善心脏病检测的潜力.

研究的目的:

开发一个更准确,更可靠的AI模型来检测心脏病.
解决现有数据集的局限性,例如小尺寸和不平衡.
评估新型混合深度学习模型与已建立的ML算法之间的性能.

主要方法:

开发了一种混合的单维卷积神经网络 (1D CNN).
利用了来自在线调查的大量数据集,并采用了功能选择算法.
1D CNN模型是根据心脏病数据进行训练和验证的,包括非冠状动脉心脏病 (非冠状动脉心脏病) 和冠状动脉心脏病 (冠状动脉心脏病).

主要成果:

拟议的1D CNN模型与人工神经网络,随机森林,AdaBoost和支持矢量机算法相比显示出更高的准确性.
非冠心病 (非冠心病) 的验证准确率为80.1%,冠心病 (冠心病) 的验证准确率为76.9%.
1D CNN在多种心脏病中表现出更好的整体性能,包括精度,假负率和假阳性率.

结论:

混合型1D CNN模型代表了人工智能驱动的心脏病检测的重大进展.
这种方法为诊断各种心脏病提供了更高的准确性和可靠性.
这些发现表明,在临床环境中,更高效,更有效的早期发现心脏病的潜力.

关键词:

1D CNN 在线播放人工智能的人工智能是人工智能.诊断诊断诊断诊断诊断诊断功能选择功能选择心脏病疾病心脏病疾病

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