An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease
An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease
复制标题
一种用于帕金森病早期诊断的高效混合核极限学习机方法
DOI：
10.1016/j.neucom.2015.07.138
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发表时间：
2016-04-05
期刊：
影响因子：
6
通讯作者：
Wang, Su-Jing
中科院分区：
文献类型：
--
作者：
Chen, Hui-Ling;Wang, Gang;Wang, Su-Jing
本文探讨了极限学习机(ELM)和核ELM(KELM)在帕金森病(PD)早期诊断中的应用潜力。在该方法中，对ELM中的隐含神经元个数和激活函数类型等关键参数以及KELM中的常量参数C和核参数Gamma进行了详细的研究。利用得到的最优参数，ELM和KELM成功地训练出用于PD诊断的最优预测模型。为了进一步提高ELM和KELM模型的性能，在构建分类模型之前采用了特征选择技术。在分类精度、灵敏度、特异度和ROC(接收器工作特征)曲线下面积(AUC)等方面，针对PD数据集对所提出的方法的有效性进行了严格评估。与以往研究中已有的方法相比，该方法通过10次交叉验证分析获得了非常有前景的分类精度，在10次交叉验证分析中，最高准确率为96.47%，平均准确率为95.97%。(C)2015爱思唯尔B.V.保留所有权利。
In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter gamma in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV. (C) 2015 Elsevier B.V. All rights reserved.