scholarly journals Prediction Model of Juvenile Football Players’ Sports Injury Based on Text Classification Technology of Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kai He
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Text Classification ◽  
Sports Injury ◽  
Ligament Injury ◽  
Football Players ◽  
Training Methods ◽  
Joint Injury ◽  
Injury Prediction ◽  
Exercise Load

As the level of soccer in our country has improved rapidly, the level of skill has gradually improved, and the requirements for training of athletes have increased. Due to changes in athlete training methods, it has been decided that athletes must bear a great risk of sports injuries. Accurate prediction of injuries is very important for the development of youth soccer. Based on this, this paper proposes a text classification algorithm based on machine learning and builds a sports injury prediction model that can accurately predict athlete injuries, reduce athlete injuries during training, and be effective. We put forward various sports suitable for young athletes, and put forward some measures to prevent and alleviate athletes’ injuries. This article selects 48 football players from a college of physical education of a university for testing. The athletes participating in the experiment use professional equipment to collect exercise volume and exercise load data, and real-time records of each athlete's physical fitness data within half a year, through the athlete's exercise volume, exercise load, body metabolism, and physical indicators to predict their sports injury. Experiments show that from the degree of injury, it can be seen that the severe injury is the least, with 5 cases of muscle injury, 2 cases of fascia ligament injury, and 1 case of joint injury. There were 25 cases of mild injuries, accounting for 41.0% of the total. This is because the athlete’s sports injury prediction model has better prediction capabilities, allowing athlete coaches and therapists to optimize training courses, ultimately preventing injuries, improving training levels, and reducing rehabilitation costs.

scholarly journals Is Foot Type an Indicator of Anterior Cruciate Ligament Injury in Football Players?

2020 ◽  
Vol 07 (02) ◽  
pp. 16-22
Author(s):  
K Zutshi ◽  
Keyword(s):  
Risk Factor ◽  
Cruciate Ligament ◽  
Sports Injury ◽  
Acl Injury ◽  
Ligament Injury ◽  
Football Players ◽  
Professional Football ◽  
Extrinsic Factors ◽  
Statistical Tool ◽  
History Of

Background: Prevention of sports injuries requires a comprehensive analysis of intrinsic and extrinsic factors of injuries in athletes. Pre-participation, evaluation, biomechanical assessment and new technology are helpful in providing useful information about the cause and mechanism of sports injury and strategies for injury prevention. However, there have been only few previous investigations which can conclusively correlate certain foot types with specific knee injury. Objective: To determine any relationship between foot type and ACL injury. Method: A case-control study design was adopted for this clinical study to investigate foot- type as a risk factor for ACL injury. 35 professional football players with a surgical history of ACL reconstruction and 35 professional football players without any history of ACL injury participated in our study. Foot types were determined by measuring their medial longitudinal arch angle and rearfoot-leg eversion angle. Questionnaire which included other variables of ACL injury were filled and analysed to eliminate their interference in this study. Odds ratio was used as reliable statistical tool to estimate the relative risk. Result: There was a significant relationship between pronated foot type as a risk factor for ACL injury. Conclusion: This suggests that pronated foot is a risk factor for ACL injury in football players.

scholarly journals A Refined Prediction Model for Core and Lower Extremity Sprains and Strains Among Collegiate Football Players

2015 ◽  
Vol 50 (6) ◽  
pp. 643-650 ◽  
Author(s):  
Gary B. Wilkerson ◽  
Marisa A. Colston
Keyword(s):  
Risk Factors ◽  
Confidence Interval ◽  
Lower Extremity ◽  
Prediction Model ◽  
Football Players ◽  
Low Back ◽  
Sprains And Strains ◽  
High Exposure ◽  
Injury Prediction ◽  
Collegiate Football

Context Researchers have identified high exposure to game conditions, low back dysfunction, and poor endurance of the core musculature as strong predictors for the occurrence of sprains and strains among collegiate football players. Objective To refine a previously developed injury-prediction model through analysis of 3 consecutive seasons of data. Design Cohort study. Setting National Collegiate Athletic Association Division I Football Championship Subdivision football program. Patients or Other Participants For 3 consecutive years, all 152 team members (age = 19.7 ± 1.5 years, height = 1.84 ± 0.08 m, mass = 101.08 ± 19.28 kg) presented for a mandatory physical examination on the day before initiation of preseason practice sessions. Main Outcome Measure(s) Associations between preseason measurements and the subsequent occurrence of a core or lower extremity sprain or strain were established for 256 player-seasons of data. We used receiver operating characteristic analysis to identify optimal cut points for dichotomous categorizations of cases as high risk or low risk. Both logistic regression and Cox regression analyses were used to identify a multivariable injury-prediction model with optimal discriminatory power. Results Exceptionally good discrimination between injured and uninjured cases was found for a 3-factor prediction model that included equal to or greater than 1 game as a starter, Oswestry Disability Index score equal to or greater than 4, and poor wall-sit–hold performance. The existence of at least 2 of the 3 risk factors demonstrated 56% sensitivity, 80% specificity, an odds ratio of 5.28 (90% confidence interval = 3.31, 8.44), and a hazard ratio of 2.97 (90% confidence interval = 2.14, 4.12). Conclusions High exposure to game conditions was the dominant injury risk factor for collegiate football players, but a surprisingly mild degree of low back dysfunction and poor core-muscle endurance appeared to be important modifiable risk factors that should be identified and addressed before participation.

scholarly journals Basketball Sports Injury Prediction Model Based on the Grey Theory Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fengyan Zhang ◽  
Ying Huang ◽  
Wengang Ren
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Sports Injuries ◽  
Sports Injury ◽  
Time Interval ◽  
Single Model ◽  
Interval Model ◽  
Injury Prediction ◽  
Combination Model ◽  
Gray Theory

Sports injuries will have an impact on the consistency and systemicity of the training process, as well as athlete training and performance improvement. Many talented athletes have had their careers cut short due to sports injuries. Preventing sports injuries is the best way for basketball players to reduce sports injuries. Many coaches and athletes on sports teams, on the other hand, are unaware of the importance of sports injury prevention. They only realize that the body’s sports functions are abnormal when it suffers from sports injuries. As a result, this paper proposes a gray theory neural network-based athlete injury prediction model. First, from the standpoint of a single model, the improved unequal interval model is used to predict sports injury by optimizing the unequal interval model in gray theory. The findings show that it is a good predictor of sports injuries, but it is a poor predictor of the average number of injuries. Following that, in order to overcome the shortcomings of a single model, a gray neural network combination model was used. A combination model of the unequal time interval model and BP neural network was determined and established. The prediction effect is significantly improved by combining the gray neural network mapping model and the coupling model to predict the two characteristics of sports injuries. Finally, simulation experiments show that the proposed method is effective.

The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*

2018 ◽  
Vol 46 (7) ◽  
pp. 1070-1077 ◽  
Author(s):  
Jay L. Koyner ◽  
Kyle A. Carey ◽  
Dana P. Edelson ◽  
Matthew M. Churpek
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Prediction Model ◽  
Kidney Injury ◽  
Injury Prediction

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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