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适应性深度SVM用于在心脏病患者中检测早期心脏病
S N Netra1, N N Srinidhi2, E Naresh3
1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
Scientific reports
|August 18, 2025
概括
这项研究引入了一种新的机器学习框架,用于早期发现心脏病发作和确定心房 (AF) 率. 这种新的方法达到96.07%的准确性,优于现有的及时治疗患者的方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心脏病,包括心脏病发作,是全球主要的死亡原因.
- 早期发现和持续监测对于降低心脏病死亡率至关重要.
- 机器学习提供了从医疗保健数据中准确检测心脏病的潜力,但大数据集仍然存在挑战.
研究的目的:
- 开发一种用于早期发现心脏病的新型机器学习框架.
- 为了准确确定患者的心房率 (AF).
- 通过使用大型医疗保健数据集来应对挑战,提高预测结果.
主要方法:
- 从基准资源收集数据,然后使用基于任意变量的船舶救援优化 (EAVSRO) 进行加权最佳特征选择.
- 使用自适应深度支持矢量机器 (AD-SVM) 检测心脏病.
- 通过自适应多尺度卷积囊网络 (AMCCNet) 确定心房动 (AF) 速率,并通过EAVSRO进行参数调整.
主要成果:
- 拟议的框架实现了96.07%的检测精度.
- 该模型与现有方法 (如CNN-LSTM,DCNN,Adaboost和SVM) 相比,表现出更高的性能.
- 证实了对心脏病的有效早期检测和AF频率的准确确定.
结论:
- 开发的框架为早期心脏病检测和AF率识别提供了有效的解决方案.
- 提高的准确性和性能表明改善了及时医疗干预的诊断能力.
- 这种方法有望改善心血管疾病管理中的患者结果.
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