School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, China; Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
School of Medicine, Taipei Medical University, Taipei 100, Taiwan.
Comput Intell Neurosci. 2016;2016:8073279. doi: 10.1155/2016/8073279. Epub 2016 May 19.
The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.
医学因素的多样性使得不确定性的分析和判断成为医学诊断的挑战之一。设计良好的医学不确定性分类和判断系统可以提高正确医疗诊断的概率。本文提出了一种新的多维分类器，它是一种基于智能算法的广义模糊小脑模型神经网络（GFCMNN）。为了获得更多关于不确定性的信息，我们使用直觉模糊语言术语来描述医学特征。通过相似性度量来获得分类的解。通过与不同评分函数的直觉模糊集（IFS）和直觉模糊交叉熵（IFCE）方法下的相同医学实例进行比较，得出了这里提出的新型分类器的优势。还进行了交叉验证实验，以进一步测试 GFCMNN 多维分类器的分类能力。所有这些实验结果都表明了所提出的 GFCMNN 多维分类器的有效性，并指出它可以协助支持与多个类别相关的正确医疗诊断。
