A quantifier-based fuzzy classification system for breast cancer patients
A quantifier-based fuzzy classification system for breast cancer patients
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DOI：
10.1016/j.artmed.2013.04.006
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发表时间：
2013-07-01
影响因子：
7.5
通讯作者：
Ellis, Ian O.
中科院分区：
文献类型：
--
作者：
Soria, Daniele;Garibaldi, Jonathan M.;Ellis, Ian O.
目的：最近对乳腺癌数据的研究已经使用免疫组织化学分析和一系列不同的聚类技术确定了七种不同的临床表型（组）。无监督分类算法之间的共识已成功地用于将患者分类为这些特定组，但通常以不对整个集合进行分类为代价。众所周知，模糊方法可以提供基于语言的分类规则。本研究的目的是调查使用模糊方法来创建一个易于解释的分类规则集，能够将大多数患者放入指定的组之一。材料和方法：本文在分析了现有文献资料的基础上，我们扩展了用于分类目的的数据驱动的基于模糊规则的系统（称为“基于模糊量化子集的算法”），并将其与新颖的类分配过程联合收割机相结合。然后将整个方法应用于由1000多名患者的10种蛋白质标记物组成的特征良好的乳腺癌数据集，以改进先前确定的组，并向临床医生提供语言规则。一系列的统计方法被用来比较所获得的类，以前获得的分组和未分类patients.Results的比例进行评估：一个规则集获得的算法，其特征是每个类一个分类规则，使用标签的高，低或省略每个生物标志物，以确定最合适的类为每个患者。当应用于整个患者集时，使用Kendall Tau与原始参考类分布进行评估时，所获得的类的分布具有0.9的一致性。在这样做时，只有38名患者的1073仍然未分类，代表一个更临床可用的类assignment algorithm.Conclusion：模糊算法提供了一个简单的解释，语言规则集，超过95%的乳腺癌患者分为七个临床组之一。(c)2013 Elsevier B.V.保留所有权利。
Objectives: Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups.Materials and methods: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm.Conclusion: The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups. (c) 2013 Elsevier B.V. All rights reserved.