基于混乱梯度的优化与模糊的时间优化CNN用于心力衰竭预测
G Kajeeth Kumar1, S Muthurajkumar2
1Department of Computer Technology, MIT Campus, Anna University, Chennai, Tamil Nadu, 600044, India. kajeethkumar7@gmail.com.
Scientific reports
|January 31, 2025
概括
一种新的心力衰竭检测模型,混乱梯度基础优化器 (CGBO) 和模糊的时间优化卷积神经网络 (FTOCNN),提供了改进的早期检测和准确性. 这种先进的系统提高了识别心力衰竭风险的可靠性,优于现有的方法.
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:
- 心力衰竭是导致过早死亡的主要原因,特别是在久坐不动的人群中.
- 早期和准确的检测对于预防心力衰竭进展至关重要.
- 现有的预测系统往往缺乏早期准确性,并且耗时.
研究的目的:
- 开发一个先进的心力衰竭检测模型,以改善早期和准确的识别.
- 为了提高心力衰竭预测的特征选择和分类准确性.
- 为了解决当前心力衰竭检测系统的局限性.
主要方法:
- 提出了一种新的混乱梯度基础优化器 (CGBO),结合混乱地图和梯度基础优化器 (GBO).
- 引入了模糊时间优化卷积神经网络 (FTOCNN) 分类器,集成CGBO和模糊时间规则.
- 使用UCI心脏数据集和电子健康记录 (EHR) 评估模型,采用统计措施,分类指标和十倍交叉验证.
主要成果:
- CGBO方法显著改善了对心力衰竭风险的特征选择.
- 使用EHR数据,FTOCNN分类器在心力衰竭检测中实现了94%的准确性.
- 与各种机器学习 (ML) 和深度学习 (DL) 分类器相比,拟的模型表现出优异的性能.
结论:
- 开发的CGBO-FTOCNN模型在早期和准确的心力衰竭检测方面取得了重大进展.
- 这种方法提高了心力衰竭预测系统的可靠性.
- 这些发现表明,通过先进的计算方法来管理心力衰竭的有希望的新方向.
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