School of Management, Shanghai University of Engineering Science, Shanghai, P.R. China.
School of Economic and Management, Shanghai University of Finance and Economics, Shanghai, P.R. China.
PLoS One. 2022 Aug 25;17(8):e0272203. doi: 10.1371/journal.pone.0272203. eCollection 2022.
In the process of medical diagnosis, a large amount of uncertain and inconsistent information is inevitably involved. There have been many fruitful results were investigated for medical diagnosis by utilizing different traditional uncertainty mathematical tools. It is found that there is limited study on measuring reliability of the information involved are rare, moreover, the existed methods cannot give the measuring reliability of every judgment to all symptoms in details.
It is quite essential to recognize the impact on the reliability of the fuzzy information provided under inadequate experience, lack of knowledge and so on. In this paper, the notion of the Z-numbers soft set is proposed to handle the reliability of every judgment to all symptoms in details. The study in this paper is an interdisciplinary approach towards rapid and efficient medical diagnosis.
An approach based on Z-numbers soft set (ZnSS)to medical diagnosis has been developed and is used to estimate whether two patterns or images are identical or approximately. The notion of Z-numbers soft set is proposed by combing the theory of soft set and Z-numbers theory. The basic properties of subset, equal, intersection, union and complement operations on the Z-numbers soft sets are defined and the similarity measure of two Z-numbers soft sets are also discussed in this paper.
An illustrative example similar to existing studies is showed to verify the effectiveness and feasibility, which can highlight the proposed method and demonstrate the solution characteristics.
Diagnosing diseases by uncertainty symptoms is not a direct and simple task at all. The approach based on ZnSS presented in this paper can not only measure reliability of the information involved, but also give the measuring reliability of every judgment to all symptoms in details.
在医学诊断过程中，不可避免地会涉及到大量不确定和不一致的信息。利用传统的不确定性数学工具，已经有许多关于医学诊断的卓有成效的研究。然而，很少有研究涉及到测量所涉及信息的可靠性，而且现有的方法无法详细地给出对所有症状的每个判断的测量可靠性。
认识到在经验不足、知识匮乏等情况下，模糊信息的可靠性所产生的影响是非常重要的。本文提出了 Z-数软集的概念，以详细地处理对所有症状的每个判断的可靠性。本研究是医学诊断的一种跨学科方法。
开发了一种基于 Z-数软集（ZnSS）的医学诊断方法，并用于估计两个模式或图像是否相同或近似。Z-数软集的概念是通过结合软集理论和 Z-数理论提出的。本文定义了 Z-数软集的子集、相等、交集、并集和补集运算的基本性质，并讨论了两个 Z-数软集的相似性度量。
展示了一个类似于现有研究的实例，以验证该方法的有效性和可行性，该实例可以突出所提出的方法，并展示解决方案的特点。
通过不确定性症状诊断疾病并非一项直接而简单的任务。本文提出的基于 ZnSS 的方法不仅可以测量所涉及信息的可靠性，还可以详细地给出对所有症状的每个判断的测量可靠性。
