Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson’s disease using digitized spiral drawings

Parkinson's disease (PD) is a neurodegenerative disorder that affects the elderly. PD affects the quality of life by causing motor and non-motor disabilities. Traditional PD diagnosis depends on the medical history, a review of symptoms, neurological and physical examinations by a medical speci...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2023, Vol.14 (1), p.175-189
Main Authors: Thakur, Mahima, Dhanalakshmi, Samiappan, Kuresan, Harisudha, Senthil, Ramalingam, Narayanamoorthi, R., Lai, Khin Wee
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation
Biomarkers
Classifiers
Computational Intelligence
Datasets
Diagnosis
Digitization
Disease
Engineering
Handwriting
Kinematics
Machine learning
Motor ability
Multilayer perceptrons
Multilayers
Original Research
Parkinson's disease
Patients
Physical examinations
Robotics and Automation
Signs and symptoms
Skills
Support vector machines
User Interfaces and Human Computer Interaction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Parkinson's disease (PD) is a neurodegenerative disorder that affects the elderly. PD affects the quality of life by causing motor and non-motor disabilities. Traditional PD diagnosis depends on the medical history, a review of symptoms, neurological and physical examinations by a medical specialist. Early detection of PD is a critical step towards providing prompt medical action. In artificial intelligence, computer-assisted methods for PD identification have recently received more attention. The present work focuses on the early detection of PD by logically analyzing time-series data collected during a spiral drawing assessment test of Parkinson’s and normal subjects using digital tablets. A preliminary machine learning approach is taken on static and dynamic drawings tests separately using logistic regression and Support Vector Machine classifier to observe accuracies. It is leveraging a recent novel strategy of employing Restricted Boltzmann machine (RBM) pipelined with multi-layer perceptron model classifier, which provides an accuracy of 95.32% by combining both static and dynamic spiral drawings assessments. The proposed approach is a successful candidate for detecting PD patients. The reported results of cost-effective computer tool-based PD symptom monitoring are helpful in telemedicine applications.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-022-04361-3