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在乳腺癌治疗中,用于风险识别和医院选择的粗立方直观模糊软关系框架
Shahida Bashir1, Muhammad Shabir2, Asma Bibi1
1Department of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan.
这项研究引入了一个新的粗立方直观的模糊软关系框架来处理医疗保健决策中的不确定性. 拟议的模型提高了诊断的可靠性和准确性,在乳腺癌风险评估中表现优于现有的方法.
科学领域:
- 决策支持系统是什么?
- 不确定性定量化 不确定性定量化
- 模糊的集合理论 模糊的集合理论
背景情况:
- 传统的模糊和粗略的设置模型不足以解决医疗保健中的模糊和犹.
- 医疗保健决策往往涉及部分信息和多维不确定性.
研究的目的:
- 提出一个新的粗的立方直观的模糊软的关系框架.
- 有效地捕捉和表示决策中的多维不确定性.
- 在复杂的医疗保健场景中提高诊断可靠性.
主要方法:
- 结合了区间值的成员资格,直觉非成员资格和软二进制关系.
- 开发内部立方直观模糊集的粗略近似运算符.
- 使用软二进制关系 (预设和后设) 进行集合近似.
- 介绍内部立方体直观主义模糊集之间的相似关系.
- 实施一个具有两个算法的决策方案.
主要成果:
- 拟议的框架同时代表会员,非会员,犹和边界近似.
- 与经典方法相比,证明了更高的诊断可靠性和决策准确性.
- 在评估中减少了边界模糊性.
- 成功应用于乳腺癌风险识别和医院选择案例研究.
结论:
- 新的框架有效地处理医疗保健决策中的复杂不确定性.
- 拟议的模型比现有的模糊,直观模糊和粗略设置方法提供了更高的性能.
- 这种方法为提高诊断准确性和减少医疗应用中的模两可提供了强大的工具.
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