单值嗜中性集下多期健康诊断方法学的进一步研究

Jason Chou, Yi-Fong Lin, Scott Shu-Cheng Lin
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引用次数: 3

摘要

在过去的几十年里,利用切线的概念和函数与相似度量和对等距离进行可靠的医疗咨询得到了广泛的研究,并产生了许多同构度量的应用。我们比较了大多数由不同研究者提出的同构测度,并将它们分为(a)最大范数和(b)单范数两类。此外,我们发现以往的研究使用单调函数来变换恒等函数,导致表达式复杂。在本研究中,我们提供了一个理论基础来解释以下研究论文提出的一个新测度与它所研究的现有测度在获得相同模式识别结果方面的同构性质。具体来说,本研究首先提出了使用最大范数、算术平均值和聚合算子的两种相似性度量,然后详细讨论了它们的数学特征。随后,提出了这些措施的简化版本,以便于应用。本研究完全涵盖了以前的两种方法,指出使用的复杂方法是不必要的。这些发现将有助于医生、患者及其家属在多次检查中获得正确的医学诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Further Study on Multiperiod Health Diagnostics Methodology under a Single-Valued Neutrosophic Set
Employing the concept and function of tangency with similarity measures and counterpart distances for reliable medical consultations has been extensively studied in the past decades and results in lots of isomorphic measures for application. We compared the majority of such isomorphic measures proposed by various researchers and classified them into (a) maximum norm and (b) one-norm categories. Moreover, we found that previous researchers used monotonic functions to transform an identity function and resulted in complicated expressions. In this study, we provide a theoretical foundation to explain the isomorphic nature of a newer measure proposed by the following research paper against its studied existing one in deriving the same pattern recognition results. Specifically, this study initially proposes two similarity measures using maximum norm, arithmetic mean, and aggregation operators and followed by a detailed discussion on their mathematical characteristics. Subsequently, a simplified version of such measures is presented for easy application. This study completely covers two previous methods to point out that the complex approaches used were unnecessary. The findings will help physicians, patients, and their family members to obtain a proper medical diagnosis during multiple examinations.
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