scholarly journals A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

2021 ◽  
Vol 20 (2) ◽  
pp. 147-163
Author(s):  
M. Mandorino ◽  
A.J. Figueiredo ◽  
G. Cima ◽  
A. Tessitore
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Injury Risk ◽  
Classification Tree ◽  
Characteristic Curve ◽  
Soccer Players ◽  
Data Mining Algorithm ◽  
Total Quality ◽  
Tree Model ◽  
Classification Tree Model

Abstract Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

scholarly journals Identification of Neuromuscular Performance Parameters as Risk Factors of Non-contact Injuries in Male Elite Youth Soccer Players: A Preliminary Study on 62 Players With 25 Non-contact Injuries

2021 ◽  
Vol 3 ◽  
Author(s):  
Mathias Kolodziej ◽  
Kevin Nolte ◽  
Marcus Schmidt ◽  
Tobias Alt ◽  
Thomas Jaitner
Keyword(s):  
Risk Factors ◽  
Decision Tree ◽  
Postural Control ◽  
Injury Risk ◽  
Decision Tree Model ◽  
Soccer Players ◽  
Performance Parameters ◽  
Tree Model ◽  
Youth Soccer ◽  
Neuromuscular Performance

Introduction: Elite youth soccer players suffer increasing numbers of injuries owing to constantly increasing physical demands. Deficits in neuromuscular performance may increase the risk of injury. Injury risk factors need to be identified and practical cut-off scores defined. Therefore, the purpose of the study was to assess neuromuscular performance parameters within a laboratory-based injury risk screening, to investigate their association with the risk of non-contact lower extremity injuries in elite youth soccer players, and to provide practice-relevant cut-off scores.Methods: Sixty-two elite youth soccer players (age: 17.2 ± 1.1 years) performed unilateral postural control exercises in different conditions, isokinetic tests of concentric and eccentric knee extension and knee flexion (60°/s), isometric tests of hip adduction and abduction, and isometric tests of trunk flexion, extension, lateral flexion and transversal rotation during the preseason period. Non-contact lower extremities injuries were documented throughout 10 months. Risk profiling was assessed using a multivariate approach utilizing a Decision Tree model [Classification and Regression Tree (CART) method].Results: Twenty-five non-contact injuries were registered. The Decision Tree model selected the COP sway, the peak torque for knee flexion concentric, the functional knee ratio and the path of the platform in that hierarchical order as important neuromuscular performance parameters to discriminate between injured and non-injured players. The classification showed a sensitivity of 0.73 and a specificity of 0.91. The relative risk was calculated at 4.2, meaning that the risk of suffering an injury is four times greater for a player, who has been classified as injured by the Decision Tree model.Conclusion: Measuring static postural control, postural control under unstable condition and the strength of the thigh seem to enable a good indication of injury risk in elite youth soccer players. However, this finding has to be taken with caution due to a small number of injury cases. Nonetheless, these preliminary results may have practical implications for future directions in injury risk screening and in planning and developing customized training programs to counteract intrinsic injury risk factors in elite youth soccer players.

Injury risk profile of amateur Irish women soccer players and players’ opinions on risk factors and prevention strategies

2021 ◽  
Author(s):  
Tahani A. Alahmad ◽  
Audrey C. Tierney ◽  
Roisin M. Cahalan ◽  
Nassr S. Almaflehi ◽  
Amanda M. Clifford
Keyword(s):  
Risk Factors ◽  
Injury Risk ◽  
Risk Profile ◽  
Soccer Players ◽  
Prevention Strategies ◽  
Irish Women

Collaborative evaluation and management of students’ health-related physical fitness: applications of cluster analysis and the classification tree

2012 ◽  
Vol 24 (2) ◽  
Author(s):  
Jou-An Chen ◽  
Chi-Chuan Shih ◽  
Pay-Fan Lin ◽  
Jin-Jong Chen ◽  
Kuan-Chia Lin
Keyword(s):  
Cluster Analysis ◽  
Physical Fitness ◽  
Classification Tree ◽  
Metabolic Risk ◽  
Tree Model ◽  
School Year ◽  
Classification Tree Model ◽  
Health Related ◽  
Risk Approach ◽  
Collaborative Evaluation

Abstract Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students’ health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students’ physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents’ health issues could provide different angles in planning health promotion projects.

scholarly journals Assessment of Injury Risk Factors in Male Youth Soccer Players

2016 ◽  
Vol 38 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Paul J. Read ◽  
Jon L. Oliver ◽  
Mark B. A. De Ste Croix ◽  
Gregory D. Myer ◽  
Rhodri S. Lloyd
Keyword(s):  
Risk Factors ◽  
Injury Risk ◽  
Soccer Players ◽  
Male Youth ◽  
Youth Soccer ◽  
Injury Risk Factors

scholarly journals A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Xiaonan Cui ◽  
Marjolein A. Heuvelmans ◽  
Grigory Sidorenkov ◽  
Yingru Zhao ◽  
Shuxuan Fan ◽  
...  
Keyword(s):  
Classification Tree ◽  
Lung Tumors ◽  
Clinical Population ◽  
Tree Model ◽  
Classification Tree Model ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct
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