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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

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Abstract

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.

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  1. Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, 48000, Muǧla, Turkey

    Hüseyin Gürüler

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Gürüler, H. A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput & Applic 28, 1657–1666 (2017). https://doi.org/10.1007/s00521-015-2142-2

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