在费尔马特模糊动态环境中为血液癌症选择最佳治疗方法的综合研究
Dilshad Alghazzawi1, Aqsa Noor2, Hanan Alolaiyan3
1Department of Mathematics, College of Science and Arts, King Abdul Aziz University, Rabigh, Saudi Arabia.
Scientific reports
|January 22, 2024
概括
这项研究引入了新的费尔马特模糊集 (FFS) 操作员,以改善对急性淋巴细胞白血病 (ALL) 等具有挑战性的癌症的决策. 通过有效处理不准确的数据,新方法有助于选择最佳的治疗策略.
科学领域:
- 计算数学是指计算数学.
- 模糊的集合理论 模糊的集合理论
- 医疗决策支持系统
背景情况:
- 癌症,包括急性淋巴细胞白血病 (ALL),由于不受控制的细胞增殖带来了挑战.
- 费尔马特的模糊集合 (FFS) 理论为管理不准确和不确定的信息提供了一个强大的框架.
- 现有的决策方法可能会与医疗数据的复杂性作斗争.
研究的目的:
- 引入新的费尔马特模糊聚合运算符:费尔马特模糊动态加权平均 (FFDWA) 和费尔马特模糊动态加权几何 (FFDWG).
- 使用FF数据开发一种系统方法来解决多属性决策 (MADM) 问题.
- 应用这些运营商来确定ALL的最佳治疗策略.
主要方法:
- 发展和详细分析FFDWA和FFDWG运营商.
- 制定一个MADM方法,整合这些新运营商.
- 拟议方法应用于对ALL治疗选择的案例研究.
- 对新方法与现有方法进行比较分析.
主要成果:
- 拟议的FFDWA和FFDWG运营商在决策中有效处理不准确的数据.
- 开发的MADM方法为复杂的问题提供了一个系统的框架.
- 一个数值示例展示了成功应用到ALL治疗优化.
- 新方法显示出与现有技术相比的有效性和优势.
结论:
- 新的费尔马特模糊聚合运算符为不确定性下决策提供了增强的能力.
- 提出的MADM方法对于选择最优的治疗方法来治疗像ALL这样具有挑战性的疾病是有效的.
- 这项研究为处理复杂,不准确数据的医疗决策支持系统提供了有价值的工具.
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