一个自动诊断模型用于检测和分类心血管疾病,基于群集智能技术
C Venkatesh1, B V V S Prasad2, Mudassir Khan3
1Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, AP, India.
Heliyon
|February 19, 2024
概括
心血管疾病 (CVD) 导致全球三分之一的死亡. 这项研究引入了一种新的深度学习模型,结合从临床数据中对早期心血管疾病检测的优化,实现99.58%的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球死亡的主要原因,造成三分之一的死亡.
- 早期诊断相关疾病对于大多数心脏疾病的恢复至关重要.
- 用于预测心血管疾病的临床数据集,由于其大尺寸和阶级不平衡,造成了挑战.
研究的目的:
- 开发一种用于早期心血管疾病检测和分类的创新模型.
- 解决临床数据集中大尺寸和阶级不平衡的挑战.
- 为心血管疾病诊断提出一个高效的决策支持系统.
主要方法:
- 使用了优化技术和深度学习分类器的组合.
- 采用数据分析技术检查患者的医疗记录.
- 合成样本和优化参数,用于增强分类器预测.
主要成果:
- 在预测心血管疾病方面达到99.58%的高准确度.
- 计算和比较PSNR,敏感性和特异性等指标与现有系统.
- 通过早期预测证明了深度学习在降低死亡率方面的潜力.
结论:
- 拟议的模型有效地帮助早期检测和分类心血管疾病.
- 优化和深度学习的整合比传统的预测技术更为进步.
- 这种辅助系统可以通过及时诊断和干预,显著改善患者的治疗结果.
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