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Shahlaei Bagheri, Javad. (1403). Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm. سامانه مدیریت نشریات علمی, 5(2), 76-86. doi: 10.22098/rsmm.2024.14524.1319
Javad Shahlaei Bagheri. "Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm". سامانه مدیریت نشریات علمی, 5, 2, 1403, 76-86. doi: 10.22098/rsmm.2024.14524.1319
Shahlaei Bagheri, Javad. (1403). 'Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm', سامانه مدیریت نشریات علمی, 5(2), pp. 76-86. doi: 10.22098/rsmm.2024.14524.1319
Shahlaei Bagheri, Javad. Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm. سامانه مدیریت نشریات علمی, 1403; 5(2): 76-86. doi: 10.22098/rsmm.2024.14524.1319

Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm

Research in Sport Management and Marketing
مقاله 6، دوره 5، شماره 2، تیر 2024، صفحه 76-86    اصل مقاله (1.04 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22098/rsmm.2024.14524.1319
نویسنده
Javad Shahlaei Bagheri*
Associate Professor of Sports Management, Allameh Tabatabai University, Tehran, Iran
چکیده
Purpose: This study aimed to predict and manage physical injuries caused by gym equipment and facilities using the SVM algorithm.

Method: This study was of a developmental-applied type. The snowball method was used to select the subjects. Subjects were asked to answer the questionnaire online and send it to friends and acquaintances of athletes. The validity of the instrument was confirmed through the opinions of university professors and convergent validity. Cronbach's alpha was used to check reliability. The sample questionnaire included 612 athletes in the age group of 18 to 60 years. 158 people were healthy, 54 people had head injuries, 211 people had leg injuries, and 189 people had hand injuries. The SVM algorithm was used to classify people. In addition, MATLAB software version 2022 was used for data analysis. The evaluation was conducted based on the clutter matrix and accuracy criteria.

Results: The results showed that the SVM algorithm can predict head, arm and leg injuries with 74.6% accuracy and 73.2% accuracy, respectively.

Conclusion: This study showed that by discovering hidden patterns and relationships in the data, this algorithm can probably be used correctly to improve the quality of sports facilities management to prevent physical injuries of athletes.
کلیدواژه‌ها
Sports Facilities؛ Prediction؛ SVM؛ Physical Injury
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