一个新的Covid-19诊断策略使用修改后的KNN分类器
Asmaa H Rabie1, Alaa M Mohamed2, M A Abo-Elsoud3
1Computers and Control Department Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Neural computing & applications
|June 26, 2023
概括
一个新的Covid-19诊断策略 (CDS) 使用增强的灰狼优化 (EGWO) 进行特征选择,并使用混合诊断方法 (HDM) 进行准确的检测. 这种方法显著改善了Covid-19患者的诊断性能.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 由于Covid-19的全球快速传播,需要准确和及时的诊断工具.
- 缺乏普遍有效的疫苗强调了精确病例识别和隔离的重要性.
- 现有的诊断方法可能需要提高速度和准确性,以有效打击大流行.
研究的目的:
- 引入一种新的Covid-19诊断策略 (CDS),以提高准确性和速度.
- 制定一个两阶段战略,包括特征选择和混合诊断方法.
- 改善COVID-19感染个体的识别和隔离.
主要方法:
- 特性选择阶段 (FSP):利用增强的灰狼优化 (EGWO),结合过器和包装技术 (FS和BGWO),以确定COVID-19的最佳实验室测试特性.
- 诊断阶段 (DP):采用混合诊断方法 (HDM),包括使用天真贝叶斯 (NB) 的权重患者阶段 (WP2) 和使用K-最近邻居 (KNN) 的诊断患者阶段 (DP2).
- EGWO将过方法与二进制灰狼优化 (BGWO) 集成,以进行强大的特征选择.
主要成果:
- 拟议的Covid-19诊断战略 (CDS) 实现了高性能指标.
- 精度达到了99%,精度88%,回忆 (灵敏度) 90%和F-测量91%.
- 实验结果表明CDS优于其他诊断策略.
结论:
- 开发的Covid-19诊断策略 (CDS) 提供了一种快速且高度准确的疾病检测方法.
- EGWO用于特征选择和HDM用于诊断的组合证明是有效的.
- 这一战略具有改善临床决策和疫情控制的巨大潜力.
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