Diagnosis of Parkinson's disease by using ANN

Cimen, Sibel; Bolat, Bulent

doi:10.1109/icgtspicc.2016.7955281

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…Researchers in (Çimen & Bolat, 2016), has proposed a comparative study using Multi-Layer Perceptron (MLP) with one hidden layer and Generalized Regression Neural Networks (GRNN) with spread parameters to classify the PD. The sound dataset is used a sample for the models.…”

Section: Literature Reviewmentioning

confidence: 99%

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

2023

EAJSE

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Parkinson's Disease (PD) is a long-standing neurodegenerative condition of the central nervous system that mainly affects the motor system and origins full or partial damage in behavior, speech, motor reflexes, mental processing, and other energetic functions. Doctors use different types of datasets such as speech, movement and images from the people to diagnose the disease. In this paper, the speech dataset is collected from people with and without PD to detect the disease. The voice recording samples are analyzed and the feature vectors are extracted from the voice samples. A supervised ANN Multi-Layer Perceptron with a backpropagation algorithm is presented to accurately diagnose and distinguish between healthy and PD individuals. Different Architecture with diverse neuron numbers in the hidden layers are tested to utilize the model and the result of each architecture is compared to select the best ANN architecture for PD recognition. So far, our model score is the highest which is 93% for the testing dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

2023

EAJSE

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“…Singh et al [ 70 ] presented a new approach for PD detection using SVM and presented an overall accuracy of 100%. Çimen and Bolat [ 60 ] focused on the vocal signals for PD diagnosis using ANN, MLP, and GRNN. The outcomes indicated that the best performance was achieved using GRNN.…”

Section: Related Workmentioning

confidence: 99%

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Nilashi

Abumalloh

Minaei‐Bidgoli

et al. 2022

Journal of Healthcare Engineering

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Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

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Parkinson’s Disease Detection Through Visual Deep Learning

Awatramani

Gupta

2020

Advances in Intelligent Systems and Computing

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Diagnosis of Parkinson's disease by using ANN

Cited by 8 publications

References 6 publications

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Parkinson’s Disease Detection Through Visual Deep Learning

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