Diagnosis of Parkinson’s disease using modified grey wolf optimization

Sharma, Prerna; Sundaram, Shirsh; Sharma, Manish; Sharma, Arun; Gupta, Deepak

doi:10.1016/j.cogsys.2018.12.002

Cited by 127 publications

(45 citation statements)

References 28 publications

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“…[21] employed chaotic bacterial foraging optimization (CBFO) with an enhanced fuzzy k-nearest neighbor (FKNN) classifier for early diagnostics of PD using vocal recording data. [22] proposed Modified Grey Wolf Optimization (MGWO) as a search strategy for feature selection, and Random forest, k-nearest neighbor classifier and decision tree for classification. [23] proposed to use weights derived from a Multi-Layer Perceptron (MLP) for feature selection, while a Lagrangian Support Vector Machine (LSVM) was used for classification, aiming for early diagnosis of PD in a medical setting.…”

Section: Related Workmentioning

confidence: 99%

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Almeida

Filho

Carneiro

et al. 2019

Pattern Recognition Letters

200

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This study investigates the processing of voice signals for detecting Parkinson's disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.

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Section: Related Workmentioning

confidence: 99%

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Almeida

Filho

Carneiro

et al. 2019

Pattern Recognition Letters

200

View full text Add to dashboard Cite

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“…Closed loop control for PDP thus provides a fully automated drug infusion without breaking down, by infusion proportional to the reduction in plasma dopamine levels. This results in the alleviation of side effects caused by incorrect doses in drug therapy [25,26].…”

Section: Resultsmentioning

confidence: 99%

Dopaminergic Centers Neurodegeneration Biochemical and radiologic approach

Cuciureanu¹,

Popescu²,

Cuciureanu³

et al. 2019

Rev. Chim.

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Parkinson s disease is the second pathological neurological condition that affects millions of people annually. Up to now there is no curative treatment for this condition. Our study was conducted on a group of 99 patients who were followed periodically, clinically and radiologically for a period of three years after diagnosis with Parkinson�s disease. The results of our study show that MRI examination can be an important tool for differential diagnosis, etiopathogenicity and the prognosis of these patients. Patient follow up is required in personalizing and improving current treatment schedules of Levodopa.

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“…The hunting mechanism of a grey wolf pack included three successive steps of encircling, hunting and attacking. To encircle a prey, the position of each individual wolf in the pack in each iteration was modeled as detailed in Equations (3)-(7) [30]:…”

Section: Grey Wolf Optimization Algorithm (Gwo)mentioning

confidence: 99%

A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine

et al. 2020

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Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE–GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE–GWO and MDE–GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE–GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.

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Diagnosis of Parkinson’s disease using modified grey wolf optimization

Cited by 127 publications

References 28 publications

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Dopaminergic Centers Neurodegeneration Biochemical and radiologic approach

A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine

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