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使用基于Tamir的复杂模糊的Aczel-Alsina WASPAS方法对残疾进展进行AI-Powered预测模型的分类

Jabbar Ahmmad¹, Meraj Ali Khan^2,3, Ibrahim Aldayel²

¹Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan. jabbarahmad1992@gmail.com.

Scientific reports

|August 13, 2025

概括

这项研究引入了一种新的复杂模糊方法来跟踪残疾进展,提高AI模型分类准确度,以便做出更好的医疗保健决策.

科学领域:

医疗保健信息学医疗保健信息学
决策科学决策科学决策科学
人工智能在医学中的应用

背景情况:

由于不确定和不准确的健康数据,跟踪残疾进展是具有挑战性的.
现有的多标准决策 (MCDM) 方法对于复杂,模糊的医疗数据是不够的.

研究的目的:

开发一种新的分类框架,用于监测残疾进展.
解决医疗数据中处理不确定性和不准确性的局限性.

主要方法:

这是一种混合方法,它结合了Tamir复杂的模糊逻辑和Aczel-Alsina加权总和产品评估 (WASPAS) 方法.
使用复杂的模糊逻辑来处理多维不确定性,使用Aczel-Alsina函数来进行灵活的聚合.
应用框架来分类人工智能驱动的残疾监测预测模型.

主要成果:

拟议的框架显著提高了残疾跟踪中的AI模型的分类准确性.
证明了医疗保健规划的增强决策支持能力.
案例研究验证了该方法的稳定性,敏感性和有效性.

结论:

新的复杂模糊WASPAS框架为残疾进展跟踪提供了一个强大的解决方案.
提高人工智能驱动的医疗保健决策的可靠性和准确性.
为管理和规划残疾人护理提供了有价值的工具.

关键词:

由人工智能驱动的模型阿切尔-阿尔西纳的t-规范和t-规范复杂的模糊集复杂的模糊集残疾条件残疾条件的情况.这是WASPAS的方法.

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