Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning
Objective: To determine the contribution of gait analysis to the differentiation and diagnosis of these diseases by examining the walking videos of individuals diagnosed with multiple sclerosis (MS) and Parkinson's disease (PD) using the deep learning method. Materials and Methods: A hybrid sy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Galenos Yayinevi
2023-12-01
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| Series: | Türk Nöroloji Dergisi |
| Subjects: | |
| Online Access: | https://jag.journalagent.com/z4/download_fulltext.asp?pdir=tjn&un=TJN-73658 |
| _version_ | 1797361211112685568 |
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| author | Sema Gül Emel Soylu Murat Terzi Muammer Türkoğlu Kübra Aslan Koca |
| author_facet | Sema Gül Emel Soylu Murat Terzi Muammer Türkoğlu Kübra Aslan Koca |
| author_sort | Sema Gül |
| collection | DOAJ |
| description | Objective: To determine the contribution of gait analysis to the differentiation and diagnosis of these diseases by examining the walking videos of individuals diagnosed with multiple sclerosis (MS) and Parkinson's disease (PD) using the deep learning method. Materials and Methods: A hybrid system based on Convolutional Neural Networks was developed for the detection of MS and PD based on gait analysis. The patients were walked on a flat surface of approximately 14 meters and video recordings were taken from the front, back and both sides during walking. Videos of a total of 28 patients, 12 PD and 16 MS patients, were used in the study. Results: In the study, the data was analyzed using machine learning techniques and the best accuracy score was obtained as 87.5%. Conclusion: The accuracy rate of machine learning models in the diagnosis, follow-up and treatment process of patients such as MS, PD and other neurological diseases has been examined and it has been concluded that it is inevitable that these methods will be used much more over time. |
| first_indexed | 2024-03-08T15:50:40Z |
| format | Article |
| id | doaj.art-5ae6839db2ef4e37b37fd29bea421b05 |
| institution | Directory Open Access Journal |
| issn | 1309-2545 |
| language | English |
| last_indexed | 2024-03-08T15:50:40Z |
| publishDate | 2023-12-01 |
| publisher | Galenos Yayinevi |
| record_format | Article |
| series | Türk Nöroloji Dergisi |
| spelling | doaj.art-5ae6839db2ef4e37b37fd29bea421b052024-01-09T06:07:03ZengGalenos YayineviTürk Nöroloji Dergisi1309-25452023-12-0129427728110.4274/tnd.2023.73658TJN-73658Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine LearningSema Gül0Emel Soylu1Murat Terzi2Muammer Türkoğlu3Kübra Aslan Koca4Ondokuz Mayıs University, Graduate Institute, Department of Neuroscience, Samsun, TurkeySamsun University Faculty of Engineering, Department of Software Engineering, Samsun, TürkiyeOndokuz Mayıs University Faculty of Medicine, Department of Neurology, Samsun, TürkiyeSamsun University Faculty of Engineering, Department of Software Engineering, Samsun, TürkiyeAdapha Artificial Intelligence R&D and Software Inc., Samsun, TürkiyeObjective: To determine the contribution of gait analysis to the differentiation and diagnosis of these diseases by examining the walking videos of individuals diagnosed with multiple sclerosis (MS) and Parkinson's disease (PD) using the deep learning method. Materials and Methods: A hybrid system based on Convolutional Neural Networks was developed for the detection of MS and PD based on gait analysis. The patients were walked on a flat surface of approximately 14 meters and video recordings were taken from the front, back and both sides during walking. Videos of a total of 28 patients, 12 PD and 16 MS patients, were used in the study. Results: In the study, the data was analyzed using machine learning techniques and the best accuracy score was obtained as 87.5%. Conclusion: The accuracy rate of machine learning models in the diagnosis, follow-up and treatment process of patients such as MS, PD and other neurological diseases has been examined and it has been concluded that it is inevitable that these methods will be used much more over time.https://jag.journalagent.com/z4/download_fulltext.asp?pdir=tjn&un=TJN-73658machine learningneurological diseasesgait analysiskinetic analysiskinematic analysis |
| spellingShingle | Sema Gül Emel Soylu Murat Terzi Muammer Türkoğlu Kübra Aslan Koca Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning Türk Nöroloji Dergisi machine learning neurological diseases gait analysis kinetic analysis kinematic analysis |
| title | Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning |
| title_full | Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning |
| title_fullStr | Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning |
| title_full_unstemmed | Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning |
| title_short | Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning |
| title_sort | making the discrimination in the walking parameters of individuals with multiple sclerosis and parkinson s disease with machine learning |
| topic | machine learning neurological diseases gait analysis kinetic analysis kinematic analysis |
| url | https://jag.journalagent.com/z4/download_fulltext.asp?pdir=tjn&un=TJN-73658 |
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