一个大数据方案用于心脏病的分类在地图减少使用水母搜索流程的优化模式启用Spinalnet
Antony Jaya Mabel Rani1, Chinnapillai Srivenkateswaran2, Gurunathan Vishnupriya3
1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Pacing and clinical electrophysiology : PACE
|May 16, 2024
概括
这项研究引入了一种优化的SpinalNet模型,使用水母搜索流程模式优化 (JSFRO) 来准确预测心脏病. 该方法有效地从大型数据集中分类心脏病,改善患者治疗结果.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 大数据分析大数据分析
背景情况:
- 心脏病带来了显著的死亡风险,需要准确的预测来有效管理患者.
- 现有的机器学习模型在与用于心脏病预测的大型医疗数据集作斗争.
- 优化模型对于处理心脏病患者护理中的大数据至关重要.
研究的目的:
- 开发用于心脏病分类的大数据方法.
- 实现一个优化的SpinalNet模型,其中包括水母搜索流程模式优化 (JSFRO).
主要方法:
- 电心电图 (ECG) 图像被转换为二进制格式.
- 一个MapReduce模型用于特征提取 (统计,形状,时间) 和分类.
- 脊髓网使用JSFRO进行训练,JSFRO是水母搜索优化 (JSO) 和流程优化 (FRO) 的混合体.
主要成果:
- 基于JSFRO的SpinalNet实现了高性能指标.
- 获得了90.8%的准确性.
- 已证明95.2%的灵敏度和93.6%的特异性.
结论:
- 拟议的基于JSFRO的SpinalNet为大数据环境中的心脏病分类提供了有效的解决方案.
- 这种优化的模型提高了心脏病状况预测的精度.
- 该方法提供了一个强大的框架,通过先进的机器学习来改善心脏病患者的治疗.
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