Orthopedic Disease Diagnosis via Machine Learning: Comparative Analysis Using Biomechanical Features for Hernia, Spondylitis, and Normal Conditions

The studied work presented here utilizes machine learning approaches to categorize orthopedic patients based on their biomechanical properties. Biomechanics, an important part of orthopedic medicine, explains how the human body moves and works. Manual classification of patients based on these characteristics is time-consuming and subjective. This research proposes a data-driven approach using machine learning algorithms such as K-Nearest Neighbor (k-NN), AdaBoost, Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Stochastic Gradient, and Gradient Boosting methods to automate the classification process. A broad dataset of biomechanical data from orthopedic patients is used to train and evaluate the effectiveness of these algorithms. The results show that the proposed approach is effective at accurately classifying patients, as measured by metrics such as accuracy, precision, recall, and F1-score. The article addresses the ramifications of these findings, emphasizing the potential for machine learning to transform orthopedic patient classification. Furthermore, it discusses data quality, model interpretability, and generalizability issues, underlining the importance of future study in this field. Finally, incorporating machine learning into clinical practice can boost diagnostic accuracy, optimize resource allocation, and improve patient outcomes in orthopedic care.