Academic Journal
Performance assessment and fitness analysis of athletes using decision tree and data mining techniques.
| Title: | Performance assessment and fitness analysis of athletes using decision tree and data mining techniques. |
|---|---|
| Authors: | Yu, Qiuqi |
| Source: | Soft Computing - A Fusion of Foundations, Methodologies & Applications; Jan2024, Vol. 28 Issue 2, p1055-1072, 18p |
| Subject Terms: | Data mining, Decision trees, Apriori algorithm, Architecture students, Databases, Physical fitness |
| Geographic Terms: | China |
| Abstract: | Recently, the rise in student numbers has led to the establishment of many new colleges and universities in China. As a result, there has been a significant increase in data collection on students' athletic skills. To manage these data, educational institutions are implementing information management systems. However, tracking sports results remain challenging since sports information may not always be collected during sports teaching. This work presents a systematic strategy to address this problem by evaluating student athletes' abilities using an Apriori algorithm, decision tree (DT), and association rule. It covers the processes for collecting data, preprocessing, selecting features, and evaluating models. The efficiency of the DT algorithm in solving classification problems, including student achievement analysis, is emphasized. The association rule algorithm is applied to figure out the correlation between students' physical fitness and their involvement in physical education. The Apriori algorithm is introduced to reduce the amount of data needed in merging item sets. Lastly, the overall architecture of the college students' physical fitness analysis system is presented. It covers the insertion of sports test scores, the calculation of total scores, and the application of DT analysis for evaluating student achievements. The process involves standardizing database information, selecting a training instance set, and determining attributes based on information gain. The efficiency of the system is evaluated in terms of accuracy, precision, recall, and F1 score. In comparison with previous works, our recommended system can track and analyze students' athletic capability, fitness, and physical ability to create personalized workout routines and monitor their health in real-time. [ABSTRACT FROM AUTHOR] |
| Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s00500-023-09527-5 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance assessment and fitness analysis of athletes using decision tree and data mining techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Qiuqi%22">Yu, Qiuqi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Jan2024, Vol. 28 Issue 2, p1055-1072, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Architecture+students%22">Architecture students</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+fitness%22">Physical fitness</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recently, the rise in student numbers has led to the establishment of many new colleges and universities in China. As a result, there has been a significant increase in data collection on students' athletic skills. To manage these data, educational institutions are implementing information management systems. However, tracking sports results remain challenging since sports information may not always be collected during sports teaching. This work presents a systematic strategy to address this problem by evaluating student athletes' abilities using an Apriori algorithm, decision tree (DT), and association rule. It covers the processes for collecting data, preprocessing, selecting features, and evaluating models. The efficiency of the DT algorithm in solving classification problems, including student achievement analysis, is emphasized. The association rule algorithm is applied to figure out the correlation between students' physical fitness and their involvement in physical education. The Apriori algorithm is introduced to reduce the amount of data needed in merging item sets. Lastly, the overall architecture of the college students' physical fitness analysis system is presented. It covers the insertion of sports test scores, the calculation of total scores, and the application of DT analysis for evaluating student achievements. The process involves standardizing database information, selecting a training instance set, and determining attributes based on information gain. The efficiency of the system is evaluated in terms of accuracy, precision, recall, and F1 score. In comparison with previous works, our recommended system can track and analyze students' athletic capability, fitness, and physical ability to create personalized workout routines and monitor their health in real-time. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00500-023-09527-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1055 Subjects: – SubjectFull: China Type: general – SubjectFull: Data mining Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Apriori algorithm Type: general – SubjectFull: Architecture students Type: general – SubjectFull: Databases Type: general – SubjectFull: Physical fitness Type: general Titles: – TitleFull: Performance assessment and fitness analysis of athletes using decision tree and data mining techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Qiuqi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 14327643 Numbering: – Type: volume Value: 28 – Type: issue Value: 2 Titles: – TitleFull: Soft Computing - A Fusion of Foundations, Methodologies & Applications Type: main |
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