Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10019 |
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| author | Suellen M. Araújo Sabrina B. M. Nery Bianca G. Magalhães Kelson James Almeida Pedro D. Gaspar |
| author_facet | Suellen M. Araújo Sabrina B. M. Nery Bianca G. Magalhães Kelson James Almeida Pedro D. Gaspar |
| author_sort | Suellen M. Araújo |
| collection | DOAJ |
| description | Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements. |
| first_indexed | 2024-03-10T23:05:21Z |
| format | Article |
| id | doaj.art-4f6525517616470eb61d9c02a08872ad |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:21Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4f6525517616470eb61d9c02a08872ad2023-11-19T09:21:53ZengMDPI AGApplied Sciences2076-34172023-09-0113181001910.3390/app131810019Disease Severity Index in Parkinson’s Disease Based on Self-Organizing MapsSuellen M. Araújo0Sabrina B. M. Nery1Bianca G. Magalhães2Kelson James Almeida3Pedro D. Gaspar4Department of Medical Sciences, University of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, PortugalDepartment of Medical Sciences, University of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, PortugalDepartment of Electromechanical Engineering, University of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, PortugalDepartment of Neurology, Federal University of Piaui, Teresina 64049-550, BrazilDepartment of Electromechanical Engineering, University of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, PortugalParkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.https://www.mdpi.com/2076-3417/13/18/10019neural networksKohonen mapsParkinson’s disease |
| spellingShingle | Suellen M. Araújo Sabrina B. M. Nery Bianca G. Magalhães Kelson James Almeida Pedro D. Gaspar Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps Applied Sciences neural networks Kohonen maps Parkinson’s disease |
| title | Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps |
| title_full | Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps |
| title_fullStr | Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps |
| title_full_unstemmed | Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps |
| title_short | Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps |
| title_sort | disease severity index in parkinson s disease based on self organizing maps |
| topic | neural networks Kohonen maps Parkinson’s disease |
| url | https://www.mdpi.com/2076-3417/13/18/10019 |
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