Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning
ObjectiveNIHSS for stroke is widely used in clinical, but it is complex and subjective. The purpose of the study is to present a quantitative evaluation method of stroke association based on multi-dimensional gait parameters by using machine learning.Methods39 ischemic stroke patients with hemiplegi...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1544372/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856524760449024 |
|---|---|
| author | Cheng Wang Cheng Wang Cheng Wang Zhou Long Zhou Long Xiang-Dong Wang Xiang-Dong Wang You-Qi Kong Li-Chun Zhou Wei-Hua Jia Pei Li Jing Wang Xiao-Juan Wang Tian Tian |
| author_facet | Cheng Wang Cheng Wang Cheng Wang Zhou Long Zhou Long Xiang-Dong Wang Xiang-Dong Wang You-Qi Kong Li-Chun Zhou Wei-Hua Jia Pei Li Jing Wang Xiao-Juan Wang Tian Tian |
| author_sort | Cheng Wang |
| collection | DOAJ |
| description | ObjectiveNIHSS for stroke is widely used in clinical, but it is complex and subjective. The purpose of the study is to present a quantitative evaluation method of stroke association based on multi-dimensional gait parameters by using machine learning.Methods39 ischemic stroke patients with hemiplegia were selected as the stroke group and 187 healthy adults from the community as the control group. Gaitboter system was used for gait analysis. Through the labeling of stroke patients by clinicians with NIHSS score, all gait parameters obtained were used to select appropriate gait parameters. By using machine learning algorithm, a discriminant model and a hierarchical model were trained.ResultsThe discriminant model was used to distinguish between healthy people and stroke patients. The overall detection accuracy of the model based on KNN, SVM and Randomforest algorithms is 92.86, 92.86 and 90.00%, respectively. The hierarchical model was used to judge the severity of stroke in stroke patients. The model based on Randomforest, SVM and AdaBoost algorithm had an overall detection accuracy of 71.43, 85.71 and 85.71%, respectively.ConclusionThe proposed stroke association quantitative evaluation method based on multi-dimensional gait parameters has the characteristics of high accuracy, objectivity, and quantification. |
| format | Article |
| id | doaj-art-b40ca8dd572541c380b87163a484ed76 |
| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-b40ca8dd572541c380b87163a484ed762025-02-12T07:26:37ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-02-011910.3389/fninf.2025.15443721544372Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learningCheng Wang0Cheng Wang1Cheng Wang2Zhou Long3Zhou Long4Xiang-Dong Wang5Xiang-Dong Wang6You-Qi Kong7Li-Chun Zhou8Wei-Hua Jia9Pei Li10Jing Wang11Xiao-Juan Wang12Tian Tian13Jinan Zhougke Ubiquitous-Intelligent Institute of Computing Technology, Jinan, ChinaShandong Academy of Intelligent Computing Technology, Shandong Institutes of Industrial Technology (SDIIT), Jinan, ChinaNingbo Institute of Information Technology Application CAS, Ningbo, ChinaJinan Zhougke Ubiquitous-Intelligent Institute of Computing Technology, Jinan, ChinaShandong Academy of Intelligent Computing Technology, Shandong Institutes of Industrial Technology (SDIIT), Jinan, ChinaBejing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, ChinaInstitute of Computing Technology (ICT) Chinese Academy of Sciences (CAS), Beijing, ChinaDepartment of Neurology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurology, Beijing Shijingshan Hospital, Shijingshan Teaching Hospital of Capital Medical University, Beijing, ChinaGeneral Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaGeneral Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaGeneral Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaGeneral Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaObjectiveNIHSS for stroke is widely used in clinical, but it is complex and subjective. The purpose of the study is to present a quantitative evaluation method of stroke association based on multi-dimensional gait parameters by using machine learning.Methods39 ischemic stroke patients with hemiplegia were selected as the stroke group and 187 healthy adults from the community as the control group. Gaitboter system was used for gait analysis. Through the labeling of stroke patients by clinicians with NIHSS score, all gait parameters obtained were used to select appropriate gait parameters. By using machine learning algorithm, a discriminant model and a hierarchical model were trained.ResultsThe discriminant model was used to distinguish between healthy people and stroke patients. The overall detection accuracy of the model based on KNN, SVM and Randomforest algorithms is 92.86, 92.86 and 90.00%, respectively. The hierarchical model was used to judge the severity of stroke in stroke patients. The model based on Randomforest, SVM and AdaBoost algorithm had an overall detection accuracy of 71.43, 85.71 and 85.71%, respectively.ConclusionThe proposed stroke association quantitative evaluation method based on multi-dimensional gait parameters has the characteristics of high accuracy, objectivity, and quantification.https://www.frontiersin.org/articles/10.3389/fninf.2025.1544372/fullquantitative evaluationgait parametersstrokeNIHSSmachine learning |
| spellingShingle | Cheng Wang Cheng Wang Cheng Wang Zhou Long Zhou Long Xiang-Dong Wang Xiang-Dong Wang You-Qi Kong Li-Chun Zhou Wei-Hua Jia Pei Li Jing Wang Xiao-Juan Wang Tian Tian Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning Frontiers in Neuroinformatics quantitative evaluation gait parameters stroke NIHSS machine learning |
| title | Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| title_full | Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| title_fullStr | Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| title_full_unstemmed | Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| title_short | Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| title_sort | quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning |
| topic | quantitative evaluation gait parameters stroke NIHSS machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1544372/full |
| work_keys_str_mv | AT chengwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT chengwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT chengwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT zhoulong quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT zhoulong quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT xiangdongwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT xiangdongwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT youqikong quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT lichunzhou quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT weihuajia quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT peili quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT jingwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT xiaojuanwang quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning AT tiantian quantitativeevaluationmethodofstrokeassociationbasedonmultidimensionalgaitparametersbyusingmachinelearning |