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Effective detection of Parkinson's disease using an adaptive fuzzy fc-nearest neighbor approach

机译：使用自适应模糊FC近邻法有效检测帕金森氏病

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摘要

In this paper, we present an effective and efficient diagnosis system based on particle swarm optimization (PSO) enhanced fuzzy k-nearest neighbor (FKNN) for Parkinson's disease (PD) diagnosis. In the proposed system, named PSO-FKNN, both the continuous version and binary version of PSO were used to perform the parameter optimization and feature selection simultaneously. On the one hand, the neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the continuous PSO. On the other hand, binary PSO is utilized to choose the most discriminative subset of features for prediction. The effectiveness of the PSO-FKNN model has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation analysis, with the mean accuracy of 97.47%. Promisingly, the proposed diagnosis system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.
机译：在本文中，我们提出了一种基于粒子群优化（PSO）增强型模糊k最近邻（FKNN）的帕金森氏病（PD）诊断有效且高效的诊断系统。在提出的名为PSO-FKNN的系统中，PSO的连续版本和二进制版本均用于同时执行参数优化和特征选择。一方面，FKNN分类器中的邻域大小k和模糊强度参数m由连续PSO自适应地指定。另一方面，二进制PSO用于选择最有区别的特征子集进行预测。已针对PD数据集严格评估了PSO-FKNN模型的有效性，包括分类准确度，灵敏度，特异性和接收器工作特性（ROC）曲线（AUC）下的面积。与以前的研究中的现有方法相比，该系统通过10倍交叉验证分析获得了迄今为止报告的最高分类精度，平均精度为97.47％。很有希望的是，所提出的诊断系统可以作为功能强大的PD诊断工具的新候选者，并具有出色的性能。

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  • 来源
    《Biomedical signal processing and control》 |2013年第4期|364-373|共10页
  • 作者

    Wan-Li Zuo; Zhi-Yan Wang; Tong Liu; Hui-Ling Chen;

  • 作者单位

    College of Computer Science and Technology, Jilin University, Changchun 130012, China,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;

    College of Computer Science and Technology, Jilin University, Changchun 130012, China,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;

    College of Computer Science and Technology, Jilin University, Changchun 130012, China,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;

    College of Physics and Electronic Information, Wenzhou University, Wenzhou, Zhejiang 325035, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Fuzzy k-nearest neighbor method; Particle swarm optimization; Feature selection; Medical diagnosis; Parkinson's disease;

    机译：模糊k近邻法;粒子群优化;功能选择;医学诊断;帕金森氏病;

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