Associative Memory Approach for the Diagnosis of Parkinson’s Disease

Acevedo, Elena; Acevedo, Antonio; Felipe, Federico

doi:10.1007/978-3-642-21587-2_12

Cited by 3 publications

(5 citation statements)

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“…Ozcift (2012) applied rotationforest (RF) ensemble of several classifiers separately by an FS strategy-based SVM ranking attributes for each class. Acevedo, Acevedo, and Felipe (2011) presented an associative approach that is Alpha-Beta BAM algorithm together with Johnson-Möbius codification to classify patients with PD. Polat (2011) applied a combination weighting method called fuzzy c-means clustering-based feature weighting (FCMFW) and k-NN classifier on the classification of PD.…”

Section: Introductionmentioning

confidence: 99%

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An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease

Gök

2013

International Journal of Systems Science

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Parkinson's disease is a disease of the central nervous system that leads to severe difficulties in motor functions. Developing computational tools for recognition of Parkinson's disease at the early stages is very desirable for alleviating the symptoms. In this paper, we developed a discriminative model based on a selected feature subset and applied several classifier algorithms in the context of disease detection. All classifier performances from the point of both stand-alone and rotation-forest ensemble approach were evaluated on a Parkinson's disease data-set according to a blind testing protocol. The new method compared to hitherto methods outperforms the state-of-the-art in terms of both predictions of accuracy (98.46%) and area under receiver operating characteristic curve (0.99) scores applying rotation-forest ensemble k-nearest neighbour classifier algorithm.

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Section: Introductionmentioning

confidence: 99%

“…e Results were obtained fromOzcift (2012). f Results were obtained fromAcevedo et al (2011). g Results were obtained fromPolat (2011).…”

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confidence: 99%

An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease

Gök

2013

International Journal of Systems Science

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“…Using FA feature reduction technique with 10-fold cross-validation, 98.32% accuracy was achieved. This accuracy value is also greater than some other studies in the literature [28][29][30][31][32][33][34]. Moreover, when 2-fold cross validation with PCA and k-NN are implemented, it is observed that the accuracy at 95.02% is better than some studies [28][29][30][31].…”

Section: Discussionmentioning

confidence: 54%

“…Battacharya et al [29] and Sakar et al [31] have used SVM and achieved 65.22% and 92.75% accuracies, respectively, Das [30] achieved 92.9% accuracy using a neural network. Polat [33] tried a new method named fuzzy c-means clustering-based feature weighting and Acevedo et al [34] tried an alpha-beta bidirectional associative memory approach and they reported the accuracies of their classifiers as 97.93% and 97.17%, respectively. Ozcift [32] applied the IBk (a k-Nearest Neighbor variant) method and attained 96.93% and Gök [35] used k-NN classifier and reached 98.46% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Polat classified patients with PD using a feature weighting method named fuzzy c-means clustering-based feature weighting (FCMFW) and k-NN classifier [33]. Acevedo used an alpha-beta bidirectional associative memory (ABBAM) approach to differentiate between patients with PD and healthy people [34]. Gök selected features and used six different classifiers: Bayes net, linear SVM, radial basis SVM, k-NN, multilayer perceptron, and K-Star [35].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease

Özkan

2016

Entropy

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Parkinson's disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5-and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application.

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Performance Analysis of Different Classifiers for Tele-Diagnosis of Parkinson’s Disease

Khare

Singh

2021

Wireless Pers Commun

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Associative Memory Approach for the Diagnosis of Parkinson’s Disease

Cited by 3 publications

References 10 publications

An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease

An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease

A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease

Performance Analysis of Different Classifiers for Tele-Diagnosis of Parkinson’s Disease

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