Design and Development of a Model for Tennis Elbow Injury Prediction and Prevention Using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Approaches

Purpose Lateral epicondylitis, commonly referred to as tennis elbow, is a frequent sports injury that poses diagnostic and management challenges. This study aims to enhance the understanding of tennis elbow mechanisms and identify key factors influencing its development. Players often self-treat and delay medical intervention, exacerbating the condition. Method This research introduces a novel approach integrating Design of Experiments (DoE) with Response Surface Methodology (RSM) and an Expert System (ES) using both Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for personalized injury prevention recommendations. This combined methodology provides valuable insights and empowers players to adopt safer playing practices, potentially reducing tennis elbow incidence. Comprehensive education for athletes, coaches, and physicians on tennis elbow management is emphasized for early diagnosis and improved treatment outcomes. Result After analysis of computing model, 99% accuracy has been achieved using ANFIS approach for tennis elbow injury prediction. The accuracy has been analyzed after prediction through multi model along with training, validation and testing of the data. Conclusion The proposed work not only offers a deeper understanding of the factors influencing tennis elbow risk but also provides personalized preventive strategies through the expert system.