A Machine Learning Based Predictive model to identify individuals at high risk of Anterior Cruciate Ligament Reinjury after reconstruction based on Clinical, Biomechanical and Demographic factors: A Retrospective Study

The study title is A machine learning based predictive model to identify individuals at high risk of Anterior Cruciate Ligament Reinjury after reconstruction based on clinical, biomechanical and demographic factors: A retrospective study. The study aims to identify significant risk factors of Anterior Cruciate Ligament reinjury after reconstruction and to develop a predictive model using machine learning algorithm to identify individuals who are at high risk of reinjury. The data will be gathered from medical records who fulfil the inclusion and exclusion criteria. A variety of demographic, clinical and biomechanical data will be compiled and used to develop the machine learning model. The outcome measures will be Beighton’s score to assess ligament laxity and assessing muscle strength of quadriceps and hamstrings. The data will be subjected to statistical analysis.