Using machine learning in healthcare is increasingly becom- ing an advanced method for predicting and treating diseases early. The significant increase in orthopedic diseases has made early disease detec- tion more crucial than ever, allowing for more effective disease preven- tion. This study aims to support healthcare professionals in the early pre- diction and classification of orthopedic diseases. To achieve this goal, we used data visualization methods to analyze the data and assess visualiza- tions using statistical results from charts. Subsequently, machine learn- ing methods, including Random Forest, Logistic Regression, k-Nearest Neighbor, and LightGBM, were applied to a dataset containing informa- tion on 310 patients, comprising six biological features describing each patient’s pelvic status and spine. The results of these algorithms were then compared, with Logistic Regression considered the algorithm that yielded the best performance, achieving an accuracy of up to 87%. In contrast, other algorithms ranged from 85% and above.