Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning
With the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze uni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4940 |
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| author | Rong Guo Rou Dong Ni Lu Lin Yu Chaoxian Chen Yonglin Che Jiajin Zhang Jianke Yang |
| author_facet | Rong Guo Rou Dong Ni Lu Lin Yu Chaoxian Chen Yonglin Che Jiajin Zhang Jianke Yang |
| author_sort | Rong Guo |
| collection | DOAJ |
| description | With the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze university students’ fitness data and develop personalized health intervention strategies. The enhanced K-means algorithm overcomes the limitations of traditional clustering approaches, leading to improved clustering accuracy and stability. Machine learning models—including Random Forest, decision trees, Gradient Boosting Trees, and logistic regression—were utilized to validate the clustering outcomes and to identify key health indicators associated with different student groups. Based on the clustering and model analysis, targeted intervention programs are proposed, such as strength training for groups with low muscular explosiveness, endurance training for those with stamina deficiencies, and flexibility exercises for groups exhibiting limited mobility. This integrated analytical framework provides a scientifically grounded tool for comprehensive health assessments and offers actionable, data-driven recommendations for student health management. Future research will focus on optimizing algorithmic performance, enhancing data diversity, and broadening the application scope to further improve the effectiveness and feasibility of health interventions. |
| format | Article |
| id | doaj-art-68cab69b8b5c4cc6a5190282c02cd4f8 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-68cab69b8b5c4cc6a5190282c02cd4f82025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-04-01159494010.3390/app15094940Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine LearningRong Guo0Rou Dong1Ni Lu2Lin Yu3Chaoxian Chen4Yonglin Che5Jiajin Zhang6Jianke Yang7College of Big Data, Yunnan Agricultural University, Kunming 650201, ChinaCenter for Sports Intelligence Innovation and Application, Yunnan Agricultural University, Kunming 650201, ChinaCenter for Sports Intelligence Innovation and Application, Yunnan Agricultural University, Kunming 650201, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming 650201, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming 650201, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming 650201, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming 650201, ChinaCenter for Sports Intelligence Innovation and Application, Yunnan Agricultural University, Kunming 650201, ChinaWith the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze university students’ fitness data and develop personalized health intervention strategies. The enhanced K-means algorithm overcomes the limitations of traditional clustering approaches, leading to improved clustering accuracy and stability. Machine learning models—including Random Forest, decision trees, Gradient Boosting Trees, and logistic regression—were utilized to validate the clustering outcomes and to identify key health indicators associated with different student groups. Based on the clustering and model analysis, targeted intervention programs are proposed, such as strength training for groups with low muscular explosiveness, endurance training for those with stamina deficiencies, and flexibility exercises for groups exhibiting limited mobility. This integrated analytical framework provides a scientifically grounded tool for comprehensive health assessments and offers actionable, data-driven recommendations for student health management. Future research will focus on optimizing algorithmic performance, enhancing data diversity, and broadening the application scope to further improve the effectiveness and feasibility of health interventions.https://www.mdpi.com/2076-3417/15/9/4940k-means algorithmmachine learningclusteringhealth interventionpersonalized trainingdata-driven health management |
| spellingShingle | Rong Guo Rou Dong Ni Lu Lin Yu Chaoxian Chen Yonglin Che Jiajin Zhang Jianke Yang Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning Applied Sciences k-means algorithm machine learning clustering health intervention personalized training data-driven health management |
| title | Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning |
| title_full | Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning |
| title_fullStr | Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning |
| title_full_unstemmed | Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning |
| title_short | Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning |
| title_sort | physical health portrait and intervention strategy of college students based on multivariate cluster analysis and machine learning |
| topic | k-means algorithm machine learning clustering health intervention personalized training data-driven health management |
| url | https://www.mdpi.com/2076-3417/15/9/4940 |
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