Abstract
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.
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The dataset is acquired for access from the publicly available UCI ML Repository https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+Tablet#
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The authors are grateful to SRM Institute of Science and Technology, Kattankulathur, Chennai, India, for providing the required research facilities.
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Thakur, M., Dhanalakshmi, S., Kuresan, H. et al. Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson’s disease using digitized spiral drawings. J Ambient Intell Human Comput 14, 175–189 (2023). https://doi.org/10.1007/s12652-022-04361-3
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DOI: https://doi.org/10.1007/s12652-022-04361-3