scholarly journals Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution

2020 ◽  
Vol 44 (2) ◽  
pp. 345-363
Author(s):  
Srishti Sahni ◽  
Vaibhav Aggarwal ◽  
Ashish Khanna ◽  
Deepak Gupta ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Evolutionary Algorithms ◽  
Weight Distribution ◽  
Bat Algorithm ◽  
Disease Diagnosis ◽  
Bee Colony ◽  
Network Weight ◽  
The Given

Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well.

scholarly journals Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks

2021 ◽  
Author(s):  
Mohamad Alissa ◽  
Michael A. Lones ◽  
Jeremy Cosgrove ◽  
Jane E. Alty ◽  
Stuart Jamieson ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Architecture ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Healthy Controls ◽  
Abnormal Movements ◽  
Drawing Tasks

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $$93.5\%$$ 93.5 % accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson’s Disease Diagnosis

2021 ◽  
Author(s):  
Yareth Gopar-Cuevas ◽  
Ana P. Duarte-Jurado ◽  
Rosa N. Diaz-Perez ◽  
Odila Saucedo-Cardenas ◽  
Maria J. Loera-Arias ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Idiopathic Parkinson’S Disease ◽  
Idiopathic Parkinson's Disease ◽  
Multiple Biomarkers

scholarly journals Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson’s Disease

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Petr G. Lokhov ◽  
Dmitry L. Maslov ◽  
Steven Lichtenberg ◽  
Oxana P. Trifonova ◽  
Elena E. Balashova
Keyword(s):  
Parkinson’S Disease ◽  
Molecular Weight ◽  
Parkinson's Disease ◽  
High Resolution Mass Spectrometry ◽  
Early Stage ◽  
Disease Diagnosis ◽  
The Body ◽  
Low Molecular Weight ◽  
Low Molecular Weight Compounds

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson’s disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since ‘omics’ tests, to which the metabolomic LDT belongs, cover a variety of them.

Changes in Body Composition Before and After Parkinson's Disease Diagnosis

2021 ◽  
Author(s):  
Shengfang Song ◽  
Zhehui Luo ◽  
Chenxi Li ◽  
Xuemei Huang ◽  
Eric J. Shiroma ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Body Composition ◽  
Disease Diagnosis ◽  
Before And After
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