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一种使用人工智能技术的新组合心脏病发作诊断 (EHAD) 模型
Bahaa El-Din Waleed1, El-Sayed M El-Kenawy2,3, Sherif Ibrahim4
1Department of Applied Health Sciences, Higher Technological Institute of Applied Health Sciences, Mansoura, Egypt. bahaaafia@std.mans.edu.e.g.
Scientific reports
|September 15, 2025
概括
一种用于诊断心脏病发作 (心肌梗塞) 的新组合模型显示,与现有方法相比,其准确性有所提高. 这种混合方法结合了多个机器学习分类器,以更快,更精确地检测心脏病发作.
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:
- 心肌梗塞 (心脏病发作) 是全球主要的死亡原因.
- 当前的机器学习和人工智能诊断方法缺乏最佳准确性.
- 改善诊断准确度对于更好的患者结果至关重要.
研究的目的:
- 为心脏病发作检测引入一种新的混合诊断模型.
- 通过组合技术提高心脏病发作诊断的准确性和速度.
主要方法:
- 开发了集体心脏病发作诊断 (EHAD) 模型.
- 使用集成分类技术 (ECT) 集成支持矢量机器 (SVM),长短期内存 (LSTM) 和人工神经网络 (ANN).
- 用员工多数投票 (MV) 进行最终决策.
主要成果:
- 与现有模型相比,EHAD模型表现出优越的性能.
- 在回忆,精度,F1得分和准确度方面,EHAD取得了很高的成绩.
- 统计分析证实了该模型的有效性.
结论:
- 拟议的EHAD模型为心脏病发作诊断提供了更准确,更有效的方法.
- 这种混合组合方法代表了人工智能驱动的心血管诊断的重大进步.
- 进一步的开发可以通过精确和及时的诊断来改善患者的结果.
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