使用复杂的双极模糊动态数据,为糖尿病选择最佳的诊断方法
Hanan Alolaiyan1, Maryam Liaqat2, Abdul Razaq2
1Department of Mathematics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
Scientific reports
|January 31, 2025
概括
这项研究在复杂的双极模糊框架中引入了新的动态聚合运算符,以改善糖尿病检测. 这些方法提高了复杂健康数据的决策准确性.
科学领域:
- 计算智能是一种计算智能.
- 决策科学科学 决策科学
- 医疗信息学医学信息学
背景情况:
- 糖尿病是一种全球代谢障碍,需要精确的诊断方法.
- 现有的决策技术与数据模糊性和时间变化作斗争.
- 复杂的双极模糊集 (CBFS) 提供了一个框架来处理周期性和双极模糊性.
研究的目的:
- 为复杂的双极模糊 (CBF) 环境开发新的动态聚合运算符.
- 引入一个系统的方法,多个属性决策 (MADM) 问题与CBF数据.
- 提高糖尿病检测方法的准确性和可靠性.
主要方法:
- 引入两个新的运算符:CBF动态顺序加权平均 (CBFDyOWA) 和CBF动态顺序加权几何 (CBFDyOWG).
- 为CBF设置制定一个修改的得分函数.
- 运营商的应用,以解决糖尿病诊断的MADM问题.
主要成果:
- 拟议的CBFDyOWA和CBFDyOWG运营商有效地处理复杂的决策场景.
- 修改后的分数函数在CBF环境中改进了现有的方法.
- 开发的技术成功地确定了糖尿病诊断最合适的方法.
结论:
- 新型动态聚合运算符为CBF环境中的MADM问题提供了强大可靠的方法.
- 这项研究表明,糖尿病诊断的决策准确性得到了显著改善.
- 与现有方法相比,开发的技术显示出稳定性和可靠性.
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