[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120154-en":3,"doc-seo-120154-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120154,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention - A Machine Learning Approach","Athlete injuries are a pervasive issue in sports, creating major impacts on performance, career longevity, and overall well-being. This study proposes a predictive modeling framework that applies machine learning to estimate athletes’ injury risk using demographic information, training records, and performance metrics. A random forest classifier identifies key predictors and assigns athletes to high- or low-risk categories. Results show improved injury prediction accuracy compared with traditional approaches, supporting data-driven, targeted prevention actions by coaches, trainers, and medical professionals.","Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention: A Machine Learning Approach  \nBlessing Nwamaka Iduh 􀀍  \nAcademic Researcher, Department of Computer Science, Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nMaryrose Ngozi Umeh  \nAssociate Professor, Department of Computer Science, Physical Sciences,  \nNnamdiAzikiwe University Awka, Nigeria  \nOvercomer Ifeanyi Anusiuba  \nAcademic Researcher, Department of Computer Science, Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nFraser Anwaitu Egba  \nAcademic Researcher, Department of Computer Science, School of Science Education, Federal College of Education (Technical), Omoku, Rivers State, in Affiliation to University of Nigeria, Nsukka, Enugu State, Nigeria  \n\n| Suggested Citation |\n| --- |\n| Iduh, B.N., Umeh, M.N., Anusiuba, O.I., & Egba, F.A.(2024) . Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention: A Machine Learning Approach. European Journal of Theoretical and Applied Sciences, 2(4), 894-906.\u003Cbr>DOI: 10.59324/ejtas.2024.2(4).73 |\n\nAbstract:  \nAthlete injuries are a pervasive issue in sports, resulting in significant consequences for athletic performance, career longevity, and overall well-being. To address this challenge, we developed a predictive modeling framework that leverages machine learning techniques to identify athletes at high risk of injury. Our approach integrates a range of athlete-specific data, including demographic, training, and performance metrics, to generate personalized injury risk profiles. A random forest classifier was employed to identify key predictors and classify athletes into high- or low-risk categories. Our results demonstrate a substantial improvement in injury prediction accuracy compared to traditional methods, highlighting the potential of  \nmachine learning in athlete injury prevention. This framework has important implications for coaches, trainers, and medical professionals, enabling targeted interventions and optimized athlete performance. Our study contributes to the growing body of research in sports analytics and machine learning, underscoring the importance of data-driven approaches in promoting athlete health and performance.  \nKeywords: Athlete Injury Prediction, Machine Learning, Predictive Modeling, Sports Analytics, Injury Prevention.  \nIntroduction  \nThe prevailing approach to sports injuries, which focuses on reactive management strategies, is a significant obstacle to overcome. Conventional methods typically involve intervening after an injury has already occurred, resulting in  \nprolonged recovery periods and, in some cases, permanent damage to an athlete's physical wellbeing. This reactive approach not only compromises optimal performance but also fails to address the underlying causes of injuries in a timely and effective manner. Furthermore, the  \nlack of individualization in existing injury prevention strategies exacerbates the issue. Athletes possess distinct physiological characteristics, training histories, and biomechanics that significantly influence their susceptibility to injuries. However, many prevention approaches adopt a generic, onesize-fits-all model, neglecting the complexities of individual athlete profiles. This lack of personalization undermines the efficacy of preventative measures and limits their impact on reducing injury risks across diverse athletic populations.  \nTo overcome these challenges, it is essential to adopt a proactive and personalized approach to injury prevention, leveraging advanced technologies and facilitating open communication channels among all stakeholders. By doing so, we can reduce the prevalence of sports injuries and enable athletes to perform at their optimal level. Athletics plays a vital role in today's world, offering entertainment and economic benefits to society. With over a billion people worldwide watching athletic events and millions participating, ","cbCaiuM2ZKbvl47e","https://ap.wps.com/l/cbCaiuM2ZKbvl47e","pdf",1118796,1,13,"English","en",105,"# Abstract\n# Introduction\n## Reactive vs. proactive injury management\n## Personalization and data-driven prevention\n## Athletics overview and injury types\n# Objectives and proposed framework","[{\"question\":\"What problem does the proposed framework address?\",\"answer\":\"It addresses the limitations of reactive, generic injury management by enabling early identification of athletes at high risk of injury.\"},{\"question\":\"How does the framework estimate injury risk?\",\"answer\":\"It integrates athlete-specific demographic, training, and performance metrics and uses a random forest classifier to classify athletes into high- or low-risk categories.\"},{\"question\":\"What is the practical value of the machine learning approach?\",\"answer\":\"It improves injury prediction accuracy and supports targeted interventions that help optimize athlete performance and safety for coaches, trainers, and medical professionals.\"}]","Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention - 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