Machine learning for early detection and severity classification in people with Parkinson’s disease
Abstract Early detection of Parkinson’s disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83975-3 |
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| author | Juseon Hwang Changhong Youm Hwayoung Park Bohyun Kim Hyejin Choi Sang-Myung Cheon |
| author_facet | Juseon Hwang Changhong Youm Hwayoung Park Bohyun Kim Hyejin Choi Sang-Myung Cheon |
| author_sort | Juseon Hwang |
| collection | DOAJ |
| description | Abstract Early detection of Parkinson’s disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features—walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS—achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD. |
| format | Article |
| id | doaj-art-71058d75f2f74515bf58ac73b428d9fb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-71058d75f2f74515bf58ac73b428d9fb2025-01-05T12:22:10ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-83975-3Machine learning for early detection and severity classification in people with Parkinson’s diseaseJuseon Hwang0Changhong Youm1Hwayoung Park2Bohyun Kim3Hyejin Choi4Sang-Myung Cheon5Department of Health Sciences, The Graduate School of Dong-A UniversityDepartment of Health Sciences, The Graduate School of Dong-A UniversityBiomechanics Laboratory, Dong-A UniversityBiomechanics Laboratory, Dong-A UniversityDepartment of Health Sciences, The Graduate School of Dong-A UniversityDepartment of Neurology, School of Medicine, Dong-A UniversityAbstract Early detection of Parkinson’s disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features—walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS—achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD.https://doi.org/10.1038/s41598-024-83975-3Parkinson’s diseaseGaitSeverityMotor symptomArtificial intelligenceMachine learning |
| spellingShingle | Juseon Hwang Changhong Youm Hwayoung Park Bohyun Kim Hyejin Choi Sang-Myung Cheon Machine learning for early detection and severity classification in people with Parkinson’s disease Scientific Reports Parkinson’s disease Gait Severity Motor symptom Artificial intelligence Machine learning |
| title | Machine learning for early detection and severity classification in people with Parkinson’s disease |
| title_full | Machine learning for early detection and severity classification in people with Parkinson’s disease |
| title_fullStr | Machine learning for early detection and severity classification in people with Parkinson’s disease |
| title_full_unstemmed | Machine learning for early detection and severity classification in people with Parkinson’s disease |
| title_short | Machine learning for early detection and severity classification in people with Parkinson’s disease |
| title_sort | machine learning for early detection and severity classification in people with parkinson s disease |
| topic | Parkinson’s disease Gait Severity Motor symptom Artificial intelligence Machine learning |
| url | https://doi.org/10.1038/s41598-024-83975-3 |
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