A Bayesian Network approach to study the relationships between several neuromuscular performance measures and dynamic postural control in futsal players.

PURPOSE: The purpose of this study was to analyse the relationship between several parameters of neuromuscular performance with dynamic postural control using a Bayesian Network Classifiers (BN) based analysis. METHODS: The y-balance test (measure of dynamic postural control), isokinetic (concentric and eccentric) knee flexion and extension strength, isometric hip abduction and adduction strength, lower extremity joint range of motion (ROM) and core stability were assessed in 44 elite male futsal players. A feature selection process was carried out before building a BN (using the Tabu search algorithm) for each leg. The BN models built were used to make belief updating processes to study the individual and concurrent contributions of the selected parameters of neuromuscular performance on dynamic postural control. RESULTS: The BNs generated using the selected features by the algorithms correlation attribute evaluator and chi squared reported the highest evaluation criteria (area under the receiver operating characteristic curve [AUC]) for the dominant (AUC = 0.899) and non-dominant (AUC = 0.879) legs, respectively. CONCLUSIONS: The BNs demonstrated that performance achieved in the y-balance test appears to be widely influenced by hip and knee flexion and ankle dorsiflexion ROM measures in the sagittal plane, as well as by measures of static but mainly dynamic core stability in the frontal plane. Therefore, training interventions aimed at improving or maintaining dynamic postural control in elite male futsal players should include, among other things, exercises that produce ROM scores equal or higher than 127° of hip flexion, 132.5° of knee flexion as well as 34° and 30.5° of ankle dorsiflexion with the knee flexed and extended, respectively. Likewise, these training interventions should also include exercises to maintain or improve both the static and dynamic (medial-lateral plane) core stability so that futsal players can achieve medial radial error values lower than 6.69 and 8.79 mm, respectively.

A Preventive Model for Muscle Injuries: A Novel Approach based on Learning Algorithms.

INTRODUCTION: The application of contemporary statistical approaches coming from Machine Learning and Data Mining environments to build more robust predictive models to identify athletes at high risk for injury might support injury prevention strategies of the future. PURPOSE: The purpose was to analyze and compare the behavior of numerous machine learning methods to select the best-performing injury risk factor model to identify athlete at risk for lower extremity muscle injuries (MUSINJ). METHODS: A total of 132 male professional soccer and handball players underwent a preseason screening evaluation that included personal, psychological, and neuromuscular measures. Furthermore, injury surveillance was used to capture all the MUSINJ occurring in the 2013/2014 seasons. The predictive ability of several models built by applying a range of learning techniques were analyzed and compared. RESULTS: There were 32 MUSINJ over the follow-up period, 21 (65.6%) of which corresponded to the hamstrings, 3 to the quadriceps (9.3%), 4 to the adductors (12.5%), and 4 to the triceps surae (12.5%). A total of 13 injures occurred during training and 19 during competition. Three players were injured twice during the observation period so the first injury was used, leaving 29 MUSINJ that were used to develop the predictive models. The model generated by the SmooteBoost technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score, 0.747; true positive rate, 65.9%; true negative rate, 79.1) and hence was considered the best for predicting MUSINJ. CONCLUSIONS: The prediction model showed moderate accuracy for identifying professional soccer and handball players at risk for MUSINJ. Therefore, the model developed might help in the decision-making process for injury prevention.