Academic Journal
NeuroSynth: Unveiling the Parkinson's Palette - A Unified Ensemble Approach for Enhanced Predictive Modeling in Spiral Drawing Analysis.
| Τίτλος: | NeuroSynth: Unveiling the Parkinson's Palette - A Unified Ensemble Approach for Enhanced Predictive Modeling in Spiral Drawing Analysis. |
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| Συγγραφείς: | Geetha, V. Jeya, Sam, Dahlia, Joshi, A. |
| Πηγή: | International Journal of Computer Information Systems & Industrial Management Applications; 2026 Supplement, Vol. 18, p604-619, 16p |
| Θεματικοί όροι: | Parkinson's disease, Machine learning, Random forest algorithms, Prediction models, Deep learning, Convolutional neural networks, Support vector machines, Differential diagnosis |
| Περίληψη: | Parkinson Disease (PD) is a progressive neurodegenerative illness, as it is marked by motor and non-motor deficiencies, and thus presupposing early and correct diagnosis to clinically manage it successfully. The present work suggests the machine learning and deep learning framework to be used in the initial prediction of PD by means of patient voice datasets. The proposed approach combines the process of preprocessing and feature extraction with such models as Support Vector Machine (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) and subsequently evaluates the performance in terms of such metrics as accuracy, precision, recall, and F1-score. As the experimental findings prove, the proposed method performs better in classification than the existing ones, which means that it can become one of the useful diagnostic instruments of clinicians. As noted in results, the application of artificial intelligence can help to improve predictability, timely diagnose, and provide personalized treatment plans to resolve Parkinson Disease. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Information Systems & Industrial Management Applications is the property of Cerebration Science Publishing Co., Limited 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 196922359 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.34991455078 |
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| Items | – Name: Title Label: Title Group: Ti Data: NeuroSynth: Unveiling the Parkinson's Palette - A Unified Ensemble Approach for Enhanced Predictive Modeling in Spiral Drawing Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Geetha%2C+V%2E+Jeya%22">Geetha, V. Jeya</searchLink><br /><searchLink fieldCode="AR" term="%22Sam%2C+Dahlia%22">Sam, Dahlia</searchLink><br /><searchLink fieldCode="AR" term="%22Joshi%2C+A%2E%22">Joshi, A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Computer Information Systems & Industrial Management Applications; 2026 Supplement, Vol. 18, p604-619, 16p – 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="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+diagnosis%22">Differential diagnosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Parkinson Disease (PD) is a progressive neurodegenerative illness, as it is marked by motor and non-motor deficiencies, and thus presupposing early and correct diagnosis to clinically manage it successfully. The present work suggests the machine learning and deep learning framework to be used in the initial prediction of PD by means of patient voice datasets. The proposed approach combines the process of preprocessing and feature extraction with such models as Support Vector Machine (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) and subsequently evaluates the performance in terms of such metrics as accuracy, precision, recall, and F1-score. As the experimental findings prove, the proposed method performs better in classification than the existing ones, which means that it can become one of the useful diagnostic instruments of clinicians. As noted in results, the application of artificial intelligence can help to improve predictability, timely diagnose, and provide personalized treatment plans to resolve Parkinson Disease. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Computer Information Systems & Industrial Management Applications is the property of Cerebration Science Publishing Co., Limited 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: 10.70917/ijcisim-2026-3937 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 604 Subjects: – SubjectFull: Parkinson's disease Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Differential diagnosis Type: general Titles: – TitleFull: NeuroSynth: Unveiling the Parkinson's Palette - A Unified Ensemble Approach for Enhanced Predictive Modeling in Spiral Drawing Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Geetha, V. Jeya – PersonEntity: Name: NameFull: Sam, Dahlia – PersonEntity: Name: NameFull: Joshi, A. IsPartOfRelationships: – BibEntity: Dates: – D: 13 M: 01 Text: 2026 Supplement Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21507988 Numbering: – Type: volume Value: 18 Titles: – TitleFull: International Journal of Computer Information Systems & Industrial Management Applications Type: main |
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