内核随机森林与黑洞优化心脏病预测使用数据融合.
Ala Saleh Alluhaidan1, Mashael Maashi2, Noha Negm3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|December 9, 2024
概括
这项研究引入了一种高效的算法,用于融合多传感器数据,以准确预测心脏病. 核子随机森林与黑洞优化 (KRF-BHO) 和XGBoost模型在测试阶段取得了高精度.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 医疗物联网 (IoMT) 可通过可穿戴传感器实现远程患者监控.
- 心脏病诊断依赖于多传感器信号融合,但现有的方法面临准确性和效率的挑战.
研究的目的:
- 开发一种高效的算法来融合多传感器信号,并对医疗数据进行分类,以准确预测心脏病.
- 解决当前诊断方法在准确性,时间消耗和效率方面的局限性.
主要方法:
- 提出了一种混合技术,将Kernel随机森林与黑洞优化 (KRF-BHO) 结合起来,用于传感器数据的融合.
- 使用XGBoost分类器来分析心声回声图像和医疗信号数据.
- 评估了多传感器数据融合模型和克利夫兰心脏病数据集.
主要成果:
- 在测试阶段使用多传感器数据融合数据集实现了95.89%的准确性.
- 在使用克利夫兰数据集的测试阶段达到96.21%的准确性.
- 在心脏病预测的培训和测试阶段都表现出高性能.
结论:
- 拟议的KRF-BHO和XGBoost混合模型为心脏病预测提供了高效和准确的解决方案.
- 这种方法有效地克服了现有方法在准确性和效率方面的局限性.
- 强调IoMT和先进机器学习在改善心血管诊断方面的潜力.
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