The impact of preschool children’s physical fitness evaluation under self organizing maps neural network
Abstract To improve the scientific accuracy and precision of children’s physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which...
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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/s41598-025-85725-5 |
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| author | Lili Wang Wenjun Zou Yuxuan Wang Denise Koh Wan Ahmad Munsif Bin Wan Pa Rujiu Gao |
| author_facet | Lili Wang Wenjun Zou Yuxuan Wang Denise Koh Wan Ahmad Munsif Bin Wan Pa Rujiu Gao |
| author_sort | Lili Wang |
| collection | DOAJ |
| description | Abstract To improve the scientific accuracy and precision of children’s physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which struggle to handle the complexity of high-dimensional, nonlinear data, resulting in a lack of precision and personalization. This study uses the SOM neural network to reduce the dimensionality of high-dimensional health data. Moreover, it integrates cluster analysis to categorize and analyze key physical fitness attributes, such as strength, flexibility, and endurance. Experimental results show that the proposed optimized model outperforms comparison models such as T-distributed stochastic neighbor embedding, density peak clustering, and deep embedded clustering in terms of performance. The accuracy for the strength dimension reaches 0.934, the F1 score is 0.862, and the area under the curve of receiver operating characteristic is 0.944. The silhouette coefficients for cluster analysis in strength, flexibility, and endurance dimensions are 0.655, 0.559, and 0.601, respectively, demonstrating good intra-class and inter-class distances. The proposed model enhances the comprehensive analysis of children’s physical fitness and provides a scientific basis for personalized health interventions, making an important contribution to research in this field. |
| format | Article |
| id | doaj-art-3de02031b2b4497796c5f93b53d53355 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3de02031b2b4497796c5f93b53d533552025-01-12T12:19:10ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85725-5The impact of preschool children’s physical fitness evaluation under self organizing maps neural networkLili Wang0Wenjun Zou1Yuxuan Wang2Denise Koh3Wan Ahmad Munsif Bin Wan Pa4Rujiu Gao5Faculty of Education, Universiti Kebangsaan MalaysiaSchool of Artificial Intelligence, Nanchang Jiaotong InstituteSchool of Physical Education, East China University of TechnologyFaculty of Education, Universiti Kebangsaan MalaysiaFaculty of Education, Universiti Kebangsaan MalaysiaFaculty of Education, Universiti Kebangsaan MalaysiaAbstract To improve the scientific accuracy and precision of children’s physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which struggle to handle the complexity of high-dimensional, nonlinear data, resulting in a lack of precision and personalization. This study uses the SOM neural network to reduce the dimensionality of high-dimensional health data. Moreover, it integrates cluster analysis to categorize and analyze key physical fitness attributes, such as strength, flexibility, and endurance. Experimental results show that the proposed optimized model outperforms comparison models such as T-distributed stochastic neighbor embedding, density peak clustering, and deep embedded clustering in terms of performance. The accuracy for the strength dimension reaches 0.934, the F1 score is 0.862, and the area under the curve of receiver operating characteristic is 0.944. The silhouette coefficients for cluster analysis in strength, flexibility, and endurance dimensions are 0.655, 0.559, and 0.601, respectively, demonstrating good intra-class and inter-class distances. The proposed model enhances the comprehensive analysis of children’s physical fitness and provides a scientific basis for personalized health interventions, making an important contribution to research in this field.https://doi.org/10.1038/s41598-025-85725-5SOM neural networkCluster analysisEvaluation of children’s physical fitnessData dimensionality reductionPersonalized health interventionsAssessment of physical fitness of young children |
| spellingShingle | Lili Wang Wenjun Zou Yuxuan Wang Denise Koh Wan Ahmad Munsif Bin Wan Pa Rujiu Gao The impact of preschool children’s physical fitness evaluation under self organizing maps neural network Scientific Reports SOM neural network Cluster analysis Evaluation of children’s physical fitness Data dimensionality reduction Personalized health interventions Assessment of physical fitness of young children |
| title | The impact of preschool children’s physical fitness evaluation under self organizing maps neural network |
| title_full | The impact of preschool children’s physical fitness evaluation under self organizing maps neural network |
| title_fullStr | The impact of preschool children’s physical fitness evaluation under self organizing maps neural network |
| title_full_unstemmed | The impact of preschool children’s physical fitness evaluation under self organizing maps neural network |
| title_short | The impact of preschool children’s physical fitness evaluation under self organizing maps neural network |
| title_sort | impact of preschool children s physical fitness evaluation under self organizing maps neural network |
| topic | SOM neural network Cluster analysis Evaluation of children’s physical fitness Data dimensionality reduction Personalized health interventions Assessment of physical fitness of young children |
| url | https://doi.org/10.1038/s41598-025-85725-5 |
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