准确的诊断COVID-19变种使用几乎初始的粗略集的准确诊断
Radwan Abu-Gdairi1, Mostafa K El-Bably2
1Department of Mathematics, Faculty of Science, Zarqa University, Zarqa 13110, Jordan.
Heliyon
|May 31, 2024
概括
新的粗略设置理论方法提高了COVID-19变种检测准确度. 这些几乎初始的粗套件为医疗应用提供了精确的诊断工具,增强了变种识别和预测.
科学领域:
- 数学理论是一种数学理论.
- 医学诊断 医学诊断 医学诊断
- 计算拓学的计算拓.
背景情况:
- 粗略设置理论需要增强的医疗诊断方法,特别是用于COVID-19变种检测.
- 现有的方法缺乏准确识别变种所需的精度.
- 拓结构为改善诊断准确性提供了潜力.
研究的目的:
- 引入精细的数学技术,基于医疗诊断的几乎初始粗集.
- 开发基于规则的分类系统,以高准确度检测COVID-19变种.
- 通过比较分析来证明拟议方法的优越性.
主要方法:
- 使用近初始粗集开发四类粗集方法的开发.
- 应用这些技术来建立基于规则的COVID-19变种分类系统.
- 对真实世界和计算机生成的数据进行严格的测试,以验证准确性.
主要成果:
- 拟议的方法在分类COVID-19变异方面实现了100%的准确性.
- 综合性比较表明,与现有方法相比,准确性更高.
- 这项研究成功地将数学结果与医学数据结合起来.
结论:
- 精确的粗集理论,特别是近初始粗集,为医学诊断提供了精确和可访问的工具.
- 开发的分类系统显示了未来在变种识别和预测方面的研究的重大前景.
- 这些方法具有超越拓学专家的更广泛应用的潜力,具有简化的实现算法.
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