Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements

Avuçlu, Emre; Elen, Abdullah

doi:10.1007/s11517-020-02260-3

Cited by 34 publications

(14 citation statements)

References 40 publications

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“…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%

Section: Literature Reviewmentioning

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%

“…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. On the other hand, our proposed PD detection model is more precise with a 0.926 precision rate.…”

Section: Performance Analysis Of Collaborative Parkinson's Detectionmentioning

confidence: 99%

“…Many recent machine learning techniques, including Naïve Bayes, proved useful in segregating subjects suffering PD from the controls. For instance, Avuçlu and Elen [ 18 ] proposed Parkinson's detection through multiple classifiers. Their experiment was conducted on various training and testing instances spanned over 22 vocal features of 195 sound samples.…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 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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