دانلود مقاله ISI انگلیسی شماره 42812
عنوان فارسی مقاله

یک سیستم هوشمند ترکیبی جدید برای تشخیص دقیق بیماری پارکینسون

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
42812 2014 10 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
A new hybrid intelligent system for accurate detection of Parkinson's disease
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computer Methods and Programs in Biomedicine, Volume 113, Issue 3, March 2014, Pages 904–913

کلمات کلیدی
بیماری پارکینسون - ویژگی های اختلال صوت - وزن ویژگی - انتخاب ویژگی - تقسیم بندی
پیش نمایش مقاله
پیش نمایش مقاله یک سیستم هوشمند ترکیبی جدید برای تشخیص دقیق بیماری پارکینسون

چکیده انگلیسی

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.

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