The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports

This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation,...

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Published in:	PloS one Vol. 20; no. 2; p. e0317414
Main Authors:	Cui, Guomei, Wang, Chuanjun
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 13.02.2025 Public Library of Science (PLoS)
Subjects:	Accuracy Adaptive algorithms Algorithms Analysis Artificial neural networks Athletes Biology and Life Sciences Biomechanical Phenomena - physiology Biomechanics Classification Computer and Information Sciences Computer Simulation Computer-generated environments Convolutional Neural Networks Data mining Decision making Decision trees Efficiency Engineering and Technology Ensemble learning Evaluation Humans Hunting Learning algorithms Machine Learning Medicine and Health Sciences Methods Neural networks Optimization Parameters Pattern recognition Pattern Recognition, Automated - methods Physical Sciences Random Forest Research and Analysis Methods Runners (Sports) Running - physiology Sensors Signal processing Simulation methods Sports Sports injuries Sprinting Support Vector Machine Support vector machines Track & field Track and Field - physiology Vision systems China
ISSN:	1932-6203, 1932-6203
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Abstract	This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.
AbstractList	This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data. This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.
Audience	Academic
Author	Cui, Guomei Wang, Chuanjun
AuthorAffiliation	University of Lagos Faculty of Engineering, NIGERIA College of Physical Education, Shandong Sport University, Rizhao, China
AuthorAffiliation_xml	– name: University of Lagos Faculty of Engineering, NIGERIA – name: College of Physical Education, Shandong Sport University, Rizhao, China
Author_xml	– sequence: 1 givenname: Guomei surname: Cui fullname: Cui, Guomei organization: College of Physical Education, Shandong Sport University, Rizhao, China – sequence: 2 givenname: Chuanjun orcidid: 0009-0009-1648-2220 surname: Wang fullname: Wang, Chuanjun organization: College of Physical Education, Shandong Sport University, Rizhao, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39946363$$D View this record in MEDLINE/PubMed
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Copyright	Copyright: © 2025 Cui, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Cui, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Cui, Wang 2025 Cui, Wang 2025 Cui, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms	Accuracy Adaptive algorithms Algorithms Analysis Artificial neural networks Athletes Biology and Life Sciences Biomechanical Phenomena - physiology Biomechanics Classification Computer and Information Sciences Computer Simulation Computer-generated environments Convolutional Neural Networks Data mining Decision making Decision trees Efficiency Engineering and Technology Ensemble learning Evaluation Humans Hunting Learning algorithms Machine Learning Medicine and Health Sciences Methods Neural networks Optimization Parameters Pattern recognition Pattern Recognition, Automated - methods Physical Sciences Random Forest Research and Analysis Methods Runners (Sports) Running - physiology Sensors Signal processing Simulation methods Sports Sports injuries Sprinting Support Vector Machine Support vector machines Track & field Track and Field - physiology Vision systems
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Title	The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports
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