The detection of Parkinson disease using the genetic algorithm and SVM classifier

Soumaya, Zayrit; Taoufiq, Belhoussine Drissi; Nsiri, Benayad; Korkmaz, Yunus; Abdelkrim, Ammoumou

doi:10.1016/j.apacoust.2020.107528

Cited by 111 publications

(37 citation statements)

References 32 publications

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“…Concerning the acoustical features, the formant frequencies are extracted by the use of LPC presented in [9], which gives an accurate presentation of speech parameters. The square summation of each sample A set of 12 MFCC is extracted by relying on a cepstral analysis of the speech signal as it is depicted in Figure 2.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…the predicting system has an accuracy of 98.68%. The features MFCC, linear predictive coding (LPC), energy, zero-crossing rate (ZCR), and Shannon entropy have been used in many speech signal studies, either for the detection of Parkinson's disease as in [8], [9] or either for recognization [10]. In the paper, Oung et al [11] proposed a detection and a classification system of the Parkinson's disease centered on empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT).…”

Section: Introductionmentioning

confidence: 99%

“…The genetic algorithm (GA) has the main role to overcome the optimization problems. In the paper [9], [10] the genetic algorithm is applied for the purpose of selecting the convenient features to reach the most accurate prediction system. In the paper, Umar and Felemban [12] used the GA to execute cyber attacks as false data injection attacks (FDIA) in the power systems.…”

Section: Introductionmentioning

confidence: 99%

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Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Nsiri

Soumaya

Taoufiq³

et al. 2022

IJECE

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<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Nsiri

Soumaya

Taoufiq³

et al. 2022

IJECE

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“…Soumaya et al [15] have tested a hybrid classification model using genetic algorithm and SVM to detect Parkinson disease. Their method attempts to give an accuracy of 80% and 72.50% using two kernels of SVM.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry

Houd

Hachem

Painvin

2021

Preprint

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The welding seams visual inspection is still manually operated by humans in different companies, so the result of the test is still highly subjective and expensive. At present, the integration of deep learning methods for welds classification is a research focus in engineering applications. This work intends to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability, two of the various metrics affecting the production lines and cost in the automotive industry. For this purpose, we implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model in order to make a more accurate classification of welding seam defects and improve both its performance and its reliability. The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%, which presents new perspectives to the developments of deep Learning explainability and interpretability.

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“…Therefore, the fitness function is built by combining the support and confidence parameters. We predefine the thresholds of minimum support, min_sup, and the minimum confidence, min_conf, for the algorithm [9].…”

Section: Fitness Functionmentioning

confidence: 99%

Agent Mining Framework for Analyzing Moroccan Olive Oil Datasets

Imane¹,

Miloud²,

Abdelmajid³

et al. 2020

IJACSA

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The detection of Parkinson disease using the genetic algorithm and SVM classifier

Cited by 111 publications

References 32 publications

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry

Agent Mining Framework for Analyzing Moroccan Olive Oil Datasets

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