Comparison of classification methods to detect the Parkinson disease

Bourouhou, Abdennaser; Jilbab, Abdelilah; Nacir, Chafik; Hammouch, Ahmed

doi:10.1109/eitech.2016.7519634

Cited by 19 publications

(12 citation statements)

References 11 publications

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“…Therefore, the proposed collaborative features on Naïve Bayes is a practical approach to Parkinson's detection. At the final stage of analysis, the proposed collaborative features-based Parkinson's detection system has been compared with the current state-of-the-art function-based methods, viz., Avuçlu and Elen [ 18 ], Bourouhou et al [ 19 ], Zhang et al [ 20 ], Meghraoui et al [ 21 ], Kadiri et al [ 22 ], Polat and Nour [ 25 ], Xiong and Lu [ 26 ] and Mekyska et al [ 28 ]. Since our approach is based on a function-based approach, most of the methods taken for comparison belong to function-based approaches such as Naïve Bayes and Support Vector Machine (SVM).…”

Section: Resultsmentioning

confidence: 99%

“…Finally, an extensive discussion has been carried out regarding the shortcoming and future direction of the proposed Parkinson's detection model. [18] Naïve Bayes 22 70.26 Bourouhou et al [19] Naïve Bayes 26 65.00 Zhang et al [20] Naïve Bayes 22 69.24 Meghraoui et al [21] Bernoulli Naïve Bayes 3 62.50 Kadiri et al [22] Support Vector Machine -73.32 Polat and Nour [25] Linear Regression 45 77.50 Xiong and Lu [26] Naïve Bayes 8 72.00 Mekyska et al [28] Classification and regression trees 8 75.19 Collaborative PD (proposed) Naïve Bayes 7 78.97…”

Section: Discussionmentioning

confidence: 99%

“…A detailed comparison through additional performance measures helps to visualize the capability of the proposed approach over other Naïve Bayes approaches. For this comparison, the Avuçlu and Elen [18] and Bourouhou et al [19] methods are taken into consideration. According to Table 10, the Avuçlu and Elen [18] method has the highest sensitivity score of 0.949. erefore, the concerned method indicates that 94.9% of Parkinson's subjects are detected among all the Parkinson's subjects.…”

Section: Performance Analysis Of Collaborative Parkinson's Detectionmentioning

confidence: 99%

“…It has been observed that the Naïve Bayes detects the Parkinson's subjects with 70.26% accuracy with a precision of 0.64. Bourouhou et al [ 19 ] compared many classifiers to predict the presence of Parkinson's among subjects. Their experiment was conducted on 40 subjects comprising 20 Parkinson's and control subjects.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Pramanik

Pradhan

Nandy

et al. 2021

Journal of Healthcare Engineering

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This article presents a machine learning approach for Parkinson’s disease detection. Potential multiple acoustic signal features of Parkinson’s and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson’s magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Performance Analysis Of Collaborative Parkinson's Detectionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Pramanik

Pradhan

Nandy

et al. 2021

Journal of Healthcare Engineering

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“…Analogous to the best feature subset identification, there are also many works in the literature comparing classifiers (see e.g., [11][12][13]), but few present a systematic quantitative comparison of features [14] and only in [9,10] were the EMG signals obtained from amputees. More importantly, detecting intended applied force has received little attention; with most studies relying on subjects with intact limbs.…”

Section: Introductionmentioning

confidence: 99%

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

Rasines

Prada²,

Bobrov³

et al. 2021

Applied Sciences

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This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

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