A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis
Joint Authors
Souza, João W. M. de
Alves, Shara S. A.
Rebouças, Elizângela de S.
Almeida, Jefferson S.
Rebouças Filho, Pedro P.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-24
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Parkinson’s disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams.
Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease.
In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template.
This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template.
The proposed approach was evaluated using various exam templates and the handwritten traces of the patient.
Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers.
In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson’s disease.
American Psychological Association (APA)
Souza, João W. M. de& Alves, Shara S. A.& Rebouças, Elizângela de S.& Almeida, Jefferson S.& Rebouças Filho, Pedro P.. 2018. A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1130836
Modern Language Association (MLA)
Souza, João W. M. de…[et al.]. A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1130836
American Medical Association (AMA)
Souza, João W. M. de& Alves, Shara S. A.& Rebouças, Elizângela de S.& Almeida, Jefferson S.& Rebouças Filho, Pedro P.. A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1130836
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130836