Utilizing Machine Learning for Orthopedic Patient Classification via Biomechanical Features

This study investigates the application of machine learning techniques for categorizing orthopedic patients according to their biomechanical characteristics. Biomechanics, an essential aspect of orthopedic medicine, provides insights into how the human body moves and functions. Manual classification of patients according to these features is time-consuming and subjective. Therefore, this research proposes a data-driven approach using machine learning algorithms, including K-Nearest Neighbor (k-NN), AdaBoost, Random Forest (RF), Support Vector Machine (SVM), Naïve Bais, Neural Network(NN), Stochastic Gradient, Gradient boosting methods to automate the classification process. A diverse dataset comprising biomechanical data from orthopedic patients is utilized to train and evaluate the efficiency of these algorithms. Results demonstrate the effectuality of the proposed approach in accurately classifying patients, with metrics such as accuracy, precision, recall, and F1-score used for evaluation. The study discusses the implications of these outputs, highlighting the prospective of machine learning to revolutionize orthopedic patient classification. Furthermore, it addresses challenges such as data quality, model interpretability, and generalizability, emphasizing the need for further research in this area. Ultimately, integrating machine learning into clinical practice can enhance diagnostic accuracy, optimize resource allocation, and improve patient outcomes in orthopedic care.