优化功能基于融合的修改级联内核极端学习机器,用于在电子医疗保健中预测心脏病
Sumit Kumar1, Kamal Kumar Gola1, Narayan Jee1
1COER University, Roorkee, Uttarakhand, 247667, India.
概括
这项研究引入了一种新的级联极端学习机器,用于准确预测心脏病 (HD),克服常见的计算和准确性挑战. 先进的模型实现了高性能指标,证明了其在临床应用中的有效性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 心血管健康 心血管健康
背景情况:
- 预测心脏病的传统方法面临诸多挑战,包括高计算成本,错误和精度降低.
- 现有的机器学习模型经常在训练效率和数据存储要求方面扎.
研究的目的:
- 提出一个创新的级联极端学习机器 (ELM) 模型,用于增强心脏病预测.
- 通过提高准确性,减少错误和提高培训能力来解决当前方法的局限性.
主要方法:
- 数据预处理涉及缺失的数据过和规范化.
- 特性提取利用了Framingham风险因子模块,然后使用犀牛羊群优化算法进行特征融合和选择.
- 进行分类时使用了 Cascaded 内核软件加上 ELM 与堆叠自动编码器,在 Python 中进行性能分析.
主要成果:
- 拟议的模型在克利夫兰-匈牙利数据集上实现了90%的准确性,94%的精度,91.3%的回忆率和92.6%的F1分数.
- 在单独的心脏病患者数据集上,获得了92.6%的准确性.
- 与现有方法相比,该模型表现出优越的性能,减少了过拟合和改善了融合.
结论:
- 开发的级联ELM模型为心脏病预测提供了强大而有效的解决方案.
- 该模型在准确性,效率和错误减少方面的改进突显了其临床采用潜力.
- 这项研究在应用机器学习用于心血管健康诊断方面取得了重大进展.
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