Ur Rahman Atiqe, Saeed Muhammad, Saeed Muhammad Haris, Zebari Dilovan Asaad, Albahar Marwan, Abdulkareem Karrar Hameed, Al-Waisy Alaa S, Mohammed Mazin Abed
Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan.
Department of Chemistry, University of Management and Technology, Lahore 54000, Pakistan.
Bioengineering (Basel). 2023 Jan 22;10(2):147. doi: 10.3390/bioengineering10020147.
Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.
易感性分析是一种智能技术，它不仅有助于决策者评估患者任何类型脑肿瘤的疑似严重程度，还能帮助他们诊断和治疗这些肿瘤。该技术在那些基于健康和资金的可用资源有限的发展中国家已被证明更有用。通过采用算术模型的基于集合的运算，即模糊参数化复杂直觉模糊超软集（FPCIFHSS），本研究旨在开发一种强大的多属性决策支持机制，用于评估患者对脑肿瘤的易感性。FPCIFHSS被认为在处理基于信息的不确定性方面更可靠且具有通用性，因为其复杂组件和模糊参数化分别旨在处理数据的周期性和可疑参数（子参数）。在所提出的FPCIFHSS易感性模型中，根据决策者基于复杂直觉模糊数（CIFN）的专家意见，针对最相关的症状（参数）对一些合适类型的脑肿瘤进行近似。在确定基于多参数元组的模糊参数化值并将CIFN转换为模糊值之后，基于将这些肿瘤与基于模糊参数化多参数元组相关联的核心矩阵来计算此类肿瘤的得分。[0, 1]内的子区间表示患者对应于这些类型脑肿瘤的易感性程度。通过观察子区间内得分值的隶属度来检查患者的易感性。
