Cascaded Deep Learning Frameworks in Contribution to the Detection of Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, or...

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Bibliographic Details
Published in:Bioengineering (Basel) 2022-03, Vol.9 (3), p.116
Main Authors: Chintalapudi, Nalini, Battineni, Gopi, Hossain, Mohmmad Amran, Amenta, Francesco
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alzheimer's disease
Artificial intelligence
Bioengineering
Biomarkers
Classification
Constipation
Datasets
Deep learning
Diagnosis
early detection
Hypotension
Long short-term memory
Machine learning
Memory
model fitting
Movement disorders
Multilayers
Neural networks
Neurodegeneration
Neurodegenerative diseases
Parkinson's disease
Patient satisfaction
Recurrent neural networks
Signs and symptoms
Stiffness
Variables
Visualization
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Summary:Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, orthostatic hypotension, memory loss, depression, pain, and sleep disturbances. The correct diagnosis of PD cannot be easy since there is no standard objective approach to it. After the incorporation of machine learning (ML) algorithms in medical diagnoses, the accuracy of disease predictions has improved. In this work, we have used three deep-learning-type cascaded neural network models based on the audial voice features of PD patients, called Recurrent Neural Networks (RNN), Multilayer Perception (MLP), and Long Short-Term Memory (LSTM), to estimate the accuracy of PD diagnosis. A performance comparison between the three models was performed on a sample of the subjects' voice biomarkers. Experimental outcomes suggested that the LSTM model outperforms others with 99% accuracy. This study has also presented loss function curves on the relevance of good-fitting models to the detection of neurodegenerative diseases such as PD.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering9030116