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首页> 外文期刊>Neural computing & applications >A novel diagnosis system for Parkinson's disease using complex-valued artificial neural network with k-means clustering feature weighting method
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A novel diagnosis system for Parkinson's disease using complex-valued artificial neural network with k-means clustering feature weighting method

机译：具有K-Means聚类功能加权方法的复合人工神经网络对帕金森病的一种新型诊断系统

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摘要

Parkinson's disease (PD) is a degenerative, central nervous system disorder. The diagnosis of PD is difficult, as there is no standard diagnostic test and a particular system that gives accurate results. Therefore, automated diagnostic systems are required to assist the neurologist. In this study, we have developed a new hybrid diagnostic system for addressing the PD diagnosis problem. The main novelty of this paper lies in the proposed approach that involves a combination of the k-means clustering-based feature weighting (KMCFW) method and a complex-valued artificial neural network (CVANN). A Parkinson dataset comprising the features obtained from speech and sound samples were used for the diagnosis of PD. PD attributes are weighted through the use of the KMCFW method. New features obtained are converted into a complex number format. These feature values are presented as an input to the CVANN. The efficiency and effectiveness of the proposed system have been rigorously evaluated against the PD dataset in terms of five different evaluation methods. Experimental results have demonstrated that the proposed hybrid system, entitled KMCFW-CVANN, significantly outperforms the other methods detailed in the literature and achieves the highest classification results reported so far, with a classification accuracy of 99.52 %. Therefore, the proposed system appears to be promising in terms of a more accurate diagnosis of PD. Also, the application confirms the conclusion that the reliability of the classification ability of a complex-valued algorithm with regard to a real-valued dataset is high.
机译：帕金森病（PD）是一种退行性的中枢神经系统障碍。 PD的诊断很困难，因为没有标准诊断测试和特定的系统，可以提供准确的结果。因此，需要自动诊断系统来帮助神经病学家。在这项研究中，我们开发了一种用于解决PD诊断问题的新的混合诊断系统。本文的主要新颖性在于所提出的方法，涉及基于K-Means聚类的特征加权（KMCFW）方法和复方人工神经网络（CVANN）的组合。包含从语音和声音样本获得的特征的帕金森数据集用于PD的诊断。 PD属性通过使用KMCFW方法来加权。获得的新功能被转换为复杂的数字格式。这些特征值被呈现为CVANN的输入。根据五种不同的评估方法，所提出的系统的效率和有效性已经严格地评估了PD数据集。实验结果表明，拟议的杂交系统题为KMCFW-CVANN，显着优于文献中详述的其他方法，实现了迄今为止报告的最高分类结果，分类准确性为99.52％。因此，拟议的系统在更准确的PD诊断方面似乎很有前景。此外，应用程序确认了结论，即复值算法关于实值数据集的复值算法的分类能力的可靠性很高。

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