Academic Journal
Detection of Parkinson's Disease Using Spiral Drawings and Machine Learning Approaches.
| Title: | Detection of Parkinson's Disease Using Spiral Drawings and Machine Learning Approaches. |
|---|---|
| Authors: | Ramírez, Axell Albano Gutiérrez, Paredes, Antonio Alarcón, Márquez, Cornelio Yáñez |
| Source: | Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 1, p58-64, 7p |
| Subject Terms: | Parkinson's disease, Machine learning, Diagnostic services, Random forest algorithms, Support vector machines, Neurodegeneration, Graphology |
| Abstract: | This paper presents the design of a pre-diagnostic system for Parkinson's Disease (PD) based on the analysis of hand-drawn spiral patterns using machine learning techniques. Parkinson's disease is a progressive neurodegenerative disorder whose early motor manifestations--such as micrographia and tremors--can be reflected in fine motor tasks like handwriting. Although handwriting disturbances are not part of the core diagnostic criteria, they are frequently observed in early stages and are recognized by the Movement Disorder Society as supportive markers. In this study, grayscale spiral drawings are preprocessed and binarized using Otsu's thresholding method. From each image, 100 equidistant pixel coordinates are extracted where the trace is present, forming structured feature vectors. These coordinates are then used to train and evaluate several machine learning classifiers, including Random Forest, k-Nearest Neighbors, and Support Vector Machines. The proposed method prioritizes simplicity, explainability, and computational efficiency, offering a lightweight yet effective tool for early Parkinson's detection. Experimental results demonstrate the model's potential as a clinically relevant and accessible diagnostic support system. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Image & Graphics (United Kingdom) is the property of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=23013699&ISBN=&volume=14&issue=1&date=20260101&spage=58&pages=58-64&title=Journal of Image & Graphics (United Kingdom)&atitle=Detection%20of%20Parkinson%27s%20Disease%20Using%20Spiral%20Drawings%20and%20Machine%20Learning%20Approaches.&aulast=Ram%C3%ADrez%2C%20Axell%20Albano%20Guti%C3%A9rrez&id=DOI:10.18178/joig.14.1.58-64 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 191444119 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.34954833984 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detection of Parkinson's Disease Using Spiral Drawings and Machine Learning Approaches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ramírez%2C+Axell+Albano+Gutiérrez%22">Ramírez, Axell Albano Gutiérrez</searchLink><br /><searchLink fieldCode="AR" term="%22Paredes%2C+Antonio+Alarcón%22">Paredes, Antonio Alarcón</searchLink><br /><searchLink fieldCode="AR" term="%22Márquez%2C+Cornelio+Yáñez%22">Márquez, Cornelio Yáñez</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 1, p58-64, 7p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Parkinson's+disease%22">Parkinson's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+services%22">Diagnostic services</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Neurodegeneration%22">Neurodegeneration</searchLink><br /><searchLink fieldCode="DE" term="%22Graphology%22">Graphology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents the design of a pre-diagnostic system for Parkinson's Disease (PD) based on the analysis of hand-drawn spiral patterns using machine learning techniques. Parkinson's disease is a progressive neurodegenerative disorder whose early motor manifestations--such as micrographia and tremors--can be reflected in fine motor tasks like handwriting. Although handwriting disturbances are not part of the core diagnostic criteria, they are frequently observed in early stages and are recognized by the Movement Disorder Society as supportive markers. In this study, grayscale spiral drawings are preprocessed and binarized using Otsu's thresholding method. From each image, 100 equidistant pixel coordinates are extracted where the trace is present, forming structured feature vectors. These coordinates are then used to train and evaluate several machine learning classifiers, including Random Forest, k-Nearest Neighbors, and Support Vector Machines. The proposed method prioritizes simplicity, explainability, and computational efficiency, offering a lightweight yet effective tool for early Parkinson's detection. Experimental results demonstrate the model's potential as a clinically relevant and accessible diagnostic support system. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Image & Graphics (United Kingdom) is the property of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.18178/joig.14.1.58-64 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 58 Subjects: – SubjectFull: Parkinson's disease Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Diagnostic services Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Neurodegeneration Type: general – SubjectFull: Graphology Type: general Titles: – TitleFull: Detection of Parkinson's Disease Using Spiral Drawings and Machine Learning Approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ramírez, Axell Albano Gutiérrez – PersonEntity: Name: NameFull: Paredes, Antonio Alarcón – PersonEntity: Name: NameFull: Márquez, Cornelio Yáñez IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23013699 Numbering: – Type: volume Value: 14 – Type: issue Value: 1 Titles: – TitleFull: Journal of Image & Graphics (United Kingdom) Type: main |
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