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图形数据科学和机器学习用于从症状中检测COVID-19感染
Eman Alqaissi1,2, Fahd Alotaibi1, Muhammad Sher Ramzan1
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究开发了一种智能诊断模型,用于使用症状早期检测COVID-19. 基于图形的随机森林模型实现了99.36%的准确性,优于其他方法.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 机器学习是机器学习.
背景情况:
- COVID-19 (冠状病毒疾病2019) 呈现出各种症状,需要早期检测.
- 传统的诊断测试在流行病期间可能面临可用性问题.
- 基于症状的智能实时诊断模型对早期COVID-19检测至关重要.
研究的目的:
- 开发一种自动,智能,快速和实时的诊断模型,以使用症状进行早期COVID-19检测.
- 用人类疾病本体学增强的COVID-19知识图 (KG) 来利用.
- 使用快速随机投影节点嵌入图形算法提取特征.
主要方法:
- 从异质文献数据和综合人类疾病本体学构建了一个COVID-19 KG.
- 应用了快速随机投影算法来提取特征.
- 开发并比较了两个基于图形的机器学习 (ML) 管道:逻辑回归 (LR) 和随机森林 (RF),具有自动超参数调整.
主要成果:
- 基于图表的射频模型表现出卓越的性能,误差率为0.0064.4.
- 在所有指标上取得高分:特异性 (98.71%),准确性 (99.36%),精确性 (99.65%),回忆力 (99.53%) 和F1得分 (99.59%).
- 射频模型的马修斯相关系数超过了LR模型的相关系数,表明更好的预测能力.
结论:
- 基于图形的射频模型有效地分类COVID-19症状,实现高检测准确度.
- 图形数据科学与ML技术相结合,提高了诊断模型的性能.
- 这种方法加速了快速准确检测传染病的创新.
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