Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest
ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1612222/full |
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| author | Qiang Wu Qiang Wu Fang Zhang Yuchang Fei Zhenfen Sima Shanshan Gong Qifeng Tong Qingchuan Jiao Hao Wu Jianqiu Gong Jianqiu Gong |
| author_facet | Qiang Wu Qiang Wu Fang Zhang Yuchang Fei Zhenfen Sima Shanshan Gong Qifeng Tong Qingchuan Jiao Hao Wu Jianqiu Gong Jianqiu Gong |
| author_sort | Qiang Wu |
| collection | DOAJ |
| description | ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. After screening predictive variables by LASSO regression, three predictive models selected using the LazyPredict package, namely logistic regression (LR), support vector machine (SVM) and random forest (RF), were established respectively. The performance parameters (accuracy, precision, recall, and F1 score) of the models were calculated, the receiver operating characteristic curve (ROC) and the decision curve analysis (DCA) were plotted to compare the performance of the three models. An explainability analysis (SHAP) was conducted on the optimal model.ResultsThe RF model performed the best, with accuracy: 0.90, precision: 0.89, recall: 0.88, F1 score: 0.86, AUC-ROC: 0.94, and the range of the threshold probability in DCA: 7%−99%. Based on the SHAP analysis of the explainability of the RF model, the contribution degrees of the early HSP predictive variables from high to low are as follows: multiple injuries, shoulder joint flexion (p), biceps tendon effusion, sensory disorder, supraspinatus tendinopathy, subluxation, diabetes, and age.ConclusionThe RF prediction model has a good predictive effect on HSP and has good clinical explainability. It can provide objective references for the early warning and stratified management of HSP. |
| format | Article |
| id | doaj-art-3a9e4ddaa1b247a0bb15e7123d2fb0ee |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-3a9e4ddaa1b247a0bb15e7123d2fb0ee2025-08-20T03:31:10ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-06-011610.3389/fneur.2025.16122221612222Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forestQiang Wu0Qiang Wu1Fang Zhang2Yuchang Fei3Zhenfen Sima4Shanshan Gong5Qifeng Tong6Qingchuan Jiao7Hao Wu8Jianqiu Gong9Jianqiu Gong10Department of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaSchool of Medicine, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Integrated Chinese and Western Medicine, The First People's Hospital of Jiashan, Jiaxing, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Gastroenterology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, ChinaSchool of Medicine, Shaoxing University, Shaoxing, Zhejiang, ChinaObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. After screening predictive variables by LASSO regression, three predictive models selected using the LazyPredict package, namely logistic regression (LR), support vector machine (SVM) and random forest (RF), were established respectively. The performance parameters (accuracy, precision, recall, and F1 score) of the models were calculated, the receiver operating characteristic curve (ROC) and the decision curve analysis (DCA) were plotted to compare the performance of the three models. An explainability analysis (SHAP) was conducted on the optimal model.ResultsThe RF model performed the best, with accuracy: 0.90, precision: 0.89, recall: 0.88, F1 score: 0.86, AUC-ROC: 0.94, and the range of the threshold probability in DCA: 7%−99%. Based on the SHAP analysis of the explainability of the RF model, the contribution degrees of the early HSP predictive variables from high to low are as follows: multiple injuries, shoulder joint flexion (p), biceps tendon effusion, sensory disorder, supraspinatus tendinopathy, subluxation, diabetes, and age.ConclusionThe RF prediction model has a good predictive effect on HSP and has good clinical explainability. It can provide objective references for the early warning and stratified management of HSP.https://www.frontiersin.org/articles/10.3389/fneur.2025.1612222/fullhemiplegic shoulder painprediction modelrandom forestsupport vector machineSHAP |
| spellingShingle | Qiang Wu Qiang Wu Fang Zhang Yuchang Fei Zhenfen Sima Shanshan Gong Qifeng Tong Qingchuan Jiao Hao Wu Jianqiu Gong Jianqiu Gong Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest Frontiers in Neurology hemiplegic shoulder pain prediction model random forest support vector machine SHAP |
| title | Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest |
| title_full | Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest |
| title_fullStr | Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest |
| title_full_unstemmed | Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest |
| title_short | Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest |
| title_sort | development and validation of an early predictive model for hemiplegic shoulder pain a comparative study of logistic regression support vector machine and random forest |
| topic | hemiplegic shoulder pain prediction model random forest support vector machine SHAP |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1612222/full |
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