A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness
Abstract Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental tou...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87794-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585831415545856 |
|---|---|
| author | Xin Zhang Zhikang Lin Song Gu |
| author_facet | Xin Zhang Zhikang Lin Song Gu |
| author_sort | Xin Zhang |
| collection | DOAJ |
| description | Abstract Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them. The construction and comparison methods of predictive models by machine learning algorithms are investigated to evaluate the level of predictive models in order to determine the optimal predictive model. The results show that the PSO-SVR model performs best in predicting athlete engagement, with a prediction accuracy of 0.9262, along with low RMSE (0.1227), MSE (0.0146) and MAE (0.0656). The prediction accuracy of the PSO-SVR model exhibits an obvious advantage. This advantage is mainly attributed to its strong generalization ability, nonlinear processing ability, and the ability to optimize and adapt to the feature space. Particularly noteworthy is that the PSO-SVR model reduces the RMSE (7.54%), MSE (17.05%), and MAE (3.53%) significantly, while improves the R 2 (1.69%), when compared to advanced algorithms such as SWO. These results indicate that the PSO-SVR model not only improves the accuracy of prediction, but also enhances the reliability of the model, making it a powerful tool for predicting athlete engagement. In summary, this study not only provides a new perspective for understanding athlete engagement, but also provides important practical guidance for improving athlete engagement and overall performance. By adopting the PSO-SVR model, we can more accurately identify and optimise the key factors affecting athlete engagement, thus bringing far-reaching implications for research and practice in sport science and related fields. |
| format | Article |
| id | doaj-art-24d00d6b667a47148cf9ee979c1d95bf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-24d00d6b667a47148cf9ee979c1d95bf2025-01-26T12:27:18ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-87794-yA machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughnessXin Zhang0Zhikang Lin1Song Gu2College of Physical Education and Health Sciences, Zhejiang Normal UniversityFaculty of Information Science and Technology, Zhongkai University of Agriculture and EngineeringCollege of Physical Education and Health Sciences, Zhejiang Normal UniversityAbstract Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them. The construction and comparison methods of predictive models by machine learning algorithms are investigated to evaluate the level of predictive models in order to determine the optimal predictive model. The results show that the PSO-SVR model performs best in predicting athlete engagement, with a prediction accuracy of 0.9262, along with low RMSE (0.1227), MSE (0.0146) and MAE (0.0656). The prediction accuracy of the PSO-SVR model exhibits an obvious advantage. This advantage is mainly attributed to its strong generalization ability, nonlinear processing ability, and the ability to optimize and adapt to the feature space. Particularly noteworthy is that the PSO-SVR model reduces the RMSE (7.54%), MSE (17.05%), and MAE (3.53%) significantly, while improves the R 2 (1.69%), when compared to advanced algorithms such as SWO. These results indicate that the PSO-SVR model not only improves the accuracy of prediction, but also enhances the reliability of the model, making it a powerful tool for predicting athlete engagement. In summary, this study not only provides a new perspective for understanding athlete engagement, but also provides important practical guidance for improving athlete engagement and overall performance. By adopting the PSO-SVR model, we can more accurately identify and optimise the key factors affecting athlete engagement, thus bringing far-reaching implications for research and practice in sport science and related fields.https://doi.org/10.1038/s41598-025-87794-yMachine learningPrediction modelAthlete engagement |
| spellingShingle | Xin Zhang Zhikang Lin Song Gu A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness Scientific Reports Machine learning Prediction model Athlete engagement |
| title | A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness |
| title_full | A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness |
| title_fullStr | A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness |
| title_full_unstemmed | A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness |
| title_short | A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness |
| title_sort | machine learning model the prediction of athlete engagement based on cohesion passion and mental toughness |
| topic | Machine learning Prediction model Athlete engagement |
| url | https://doi.org/10.1038/s41598-025-87794-y |
| work_keys_str_mv | AT xinzhang amachinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness AT zhikanglin amachinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness AT songgu amachinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness AT xinzhang machinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness AT zhikanglin machinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness AT songgu machinelearningmodelthepredictionofathleteengagementbasedoncohesionpassionandmentaltoughness |