This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

中文翻译：

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用于医学数据分类的高效内核极限学习机和基于神经元 C-means 的属性加权方法

本文提出了一种集成系统中智基于 C 均值的属性加权内核极限学习机 (NCMAW-KELM)，用于使用 NCM 聚类和 KELM 进行医学数据分类。为此，开发了NCMAW，然后结合分类方法对医学数据进行分类。建议的方法包含两个步骤。第一步，使用 NCMAW 方法对输入属性进行加权。加权方法的目的有两个：（i）提高医学数据分类中的分类性能，（ii）从非线性可分数据集转换为线性可分数据集。最后，KELM算法用于医学数据分类目的。在 KELM 算法中，使用了四种类型的核，例如多项式、Sigmoid、径向基函数和线性。我们三个数据集的模拟结果表明，在大多数情况下，sigmoid 内核的性能优于 ELM。从结果来看，NCMAW-KELM 方法可能是医学数据分类问题中一种很有前途的方法。