一种基于代数神经测量的神经诊断推荐系统
A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures
摘要：
中性集合具有更准确地处理不确定、不完全、不一致、不确定信息的能力。本文提出了一种基于语义集的疾病预测语义推荐系统，包括单准则语义推荐系统（SC-NRS）和多准则语义推荐系统（MC-NRS）。进一步，我们研究了经典推荐系统中的并、补、交、概率和、粗和、粗交、有界差、对称差、最小和最大算子的凸线性和、笛卡尔积、结合性、交换性和分配性等代数运算。在此基础上，我们研究了格、Kleen代数、de Morgan代数、Brouwerian代数、BCK代数、Stone代数和MV代数等代数结构。此外，我们还介绍了几种基于这些代数运算的相似性度量，并研究了它们的一些理论性质。此外，我们完成了一个预测公式，使用所提出的代数相似性度量。我们还提出了一个新的算法的医疗诊断的基础上，个性化推荐系统。最后，为了验证所提出的方法的有效性，我们在心脏，RHC，乳腺癌，糖尿病和DMD数据集上进行了实验。最后，通过与ICSM、DSM、CARE、CFMD等算法以及变体变体67、变体69和变体71的比较，给出了算法的均方误差和计算时间，并以表格和图形的形式分析了算法的效率和精度。最后通过方差分析对8种算法的性能进行了分析。
英文摘要：
Neutrosophic set has the ability to handle uncertain, incomplete, inconsistent, indeterminate information in a more accurate way. In this paper, we proposed a neutrosophic recommender system to predict the diseases based on neutrosophic set which includes single-criterion neutrosophic recommender system (SC-NRS) and multi-criterion neutrosophic recommender system (MC-NRS). Further, we investigated some algebraic operations of neutrosophic recommender system such as union, complement, intersection, probabilistic sum, bold sum, bold intersection, bounded difference, symmetric difference, convex linear sum of min and max operators, Cartesian product, associativity, commutativity and distributive. Based on these operations, we studied the algebraic structures such as lattices, Kleen algebra, de Morgan algebra, Brouwerian algebra, BCK algebra, Stone algebra and MV algebra. In addition, we introduced several types of similarity measures based on these algebraic operations and studied some of their theoretic properties. Moreover, we accomplished a prediction formula using the proposed algebraic similarity measure. We also proposed a new algorithm for medical diagnosis based on neutrosophic recommender system. Finally to check the validity of the proposed methodology, we made experiments on the datasets Heart, RHC, Breast cancer, Diabetes and DMD. At the end, we presented the MSE and computational time by comparing the proposed algorithm with the relevant ones such as ICSM, DSM, CARE, CFMD, as well as other variants namely Variant 67, Variant 69, and Varian 71 both in tabular and graphical form to analyze the efficiency and accuracy. Finally we analyzed the strength of all 8 algorithms by ANOVA statistical tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
