Purpose

In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.

Design/methodology/approach

Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.

Findings

In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.

Research limitations/implications

The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.

Originality/value

The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

中文翻译：

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使用粗灰建模设计医疗规则模型系统

目的

在这项研究中，设计了一种基于粗糙集和灰色集的混合规则模型，用于诊断一种称为多发性骨髓瘤（MM）的血液癌症。灰色聚类方法用于组合相同的条件属性并提高最终模型的有效性。

设计/方法/方法

本研究使用了粗糙集理论（RST）和灰色关联分析（GIA）的一些工具来分析血清蛋白电泳（SPE）测试结果。基于患者的实验室SPE测试结果提取基于RST的规则模型。另外，使用一个决策属性和15个条件属性来提取规则。由于数据补充，离散化，约简和规则生成的算法不同，因此构建了大约四个规则模型。在接下来的阶段中，通过使用灰色聚类方法将条件属性聚类为七个聚类，通过手动离散化来减少决策属性的值集，并增加观察次数以提高模型的准确性。交叉验证用于评估模型结果，最后，

发现

本文结合灰色聚类和RST模型，提出了一种新的高精度规则模型，用于MM疾病的诊断。同样，从不同的决策表中提取了四个主要规则模型和四个改进的规则模型，以定义患者的SPE测试结果。改进模型的最大平均准确度等于95％，并且与伽玛球蛋白百分比属性/对象相关还原（GA / ORR）模型有关。

研究局限/意义

规则提取的观察总数为115，可以通过进一步采样来改善结果。为了使设计好的专家系统在实验室中方便使用，正在构建新的计算机软件，以将数据从电泳仪自动导入到生成的规则模型系统中。

创意/价值

本文的主要创意是将RST和GST一起使用来设计和创建用于诊断MM的混合规则模型。尽管已经在设计医学和癌症诊断专家系统方面进行了许多研究，但是在设计用于诊断MM的系统方面尚未发现任何研究。另一方面，使用灰色聚类方法组合条件属性是一种提高规则模型准确性的新颖解决方案。