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- 저자 : Debra Ann Dawson (검색결과 1 건)
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Salim Lahmiri,Debra Ann Dawson,Amir Shmuel 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.1
Parkinson’s disease (PD) is a widespreaddegenerative syndrome that affects the nervous system. Itsearly appearing symptoms include tremor, rigidity, andvocal impairment (dysphonia). Consequently, speechindicators are important in the identification of PD basedon dysphonic signs. In this regard, computer-aided-diagnosissystems based on machine learning can be useful inassisting clinicians in identifying PD patients. In this work,we evaluate the performance of machine learning basedtechniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were consideredand trained with a set of twenty-two voice disordermeasurements to classify healthy and PD patients. Thesemachine learning methods included linear discriminantanalysis (LDA), k nearest-neighbors (k-NN), naı¨ve Bayes(NB), regression trees (RT), radial basis function neuralnetworks (RBFNN), support vector machine (SVM), andMahalanobis distance classifier. We evaluated the performanceof these methods by means of a tenfold cross validationprotocol. Experimental results show that the SVMclassifier achieved higher average performance than allother classifiers in terms of overall accuracy, G-mean, andarea under the curve of the receiver operating characteristicplot. The SVM classifier achieved higher performancemeasures than the majority of the other classifiers also interms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest averageprecision. The RBFNN method yielded the highestF-measure.; however, it performed poorly in terms of otherperformance metrics. Finally, t tests were performed toevaluate statistical significance of the results, confirmingthat the SVM outperformed most of the other classifiers onthe majority of performance measures. SVM is a promisingmethod for identifying PD patients based on classificationof dysphonia measurements.
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