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在南非使用复杂的概率犹模糊的N-软聚合信息来识别精神障碍

Shahzaib Ashraf¹, Muneeba Kousar², Gilbert Chambashi³

¹Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, 64200, Pakistan. shahzaibashraf@kfueit.edu.pk.

Scientific reports

|November 17, 2023

概括

这项研究引入了一种新的复杂概率犹模糊的N-软集 (CPHFNSS) 来改善心理障碍的识别. 这种方法提高了医疗专业人员的诊断精度,特别是在资源不足的地区.

科学领域:

计算智能是一种计算智能.
医疗信息学医疗信息学
公共卫生公共卫生

背景情况:

心理健康障碍是越来越严重的公共卫生问题,对像南非这样的中等收入国家产生重大影响.
资金不足和复杂的患者需求在精神卫生保健决策中带来了重大挑战.
精确识别精神疾病对于有效的治疗和患者管理至关重要.

研究的目的:

介绍一个新的数学框架,复杂的概率犹不决的模糊N-软集 (CPHFNSS),用于模拟精神障碍识别中的不确定性.
通过专家知识,提高与各种心理健康状况相关的特征识别的精度.
开发和验证临床诊断的决策算法.

主要方法:

开发CPHFNSS的基本操作,包括扩展和限制交叉点和联盟,以及各种补充类型.
介绍聚合运算符与理论证明和定理的CPHFNSS.
创建一个新的评分功能,以确定最佳选择,以及使用CPHFNS数据进行诊断决策的算法.

主要成果:

拟议的CPHFNSS框架有效地模拟了精神障碍识别中的不可预测性和不确定性.
开发的聚合运算符和评分功能有助于选择最佳的特征组合用于诊断.
该算法在临床决策支持的比较分析中证明了可行性和有效性.

结论:

CPHFNSS模型提供了一个强大而有效的工具,用于提高心理障碍识别的准确性.
这种方法具有显著的潜力,可以在资源有限的环境中增强诊断能力.
这项研究证实了拟议的数学框架在心理健康临床决策中的实际实用性.

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