  This study explored the potential of gait analysis as a tool for assessing
post-injury complications, e.g., infection, malunion, or hardware irritation,
in patients with lower extremity fractures. The research focused on the
proficiency of supervised machine learning models predicting complications
using consecutive gait datasets. We identified patients with lower extremity
fractures at an academic center. Patients underwent gait analysis with a
chest-mounted IMU device. Using software, raw gait data was preprocessed,
emphasizing 12 essential gait variables. Machine learning models including
XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,
tested, and evaluated. Attention was given to class imbalance, addressed using
SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait
variables, independent of the time difference between gait analyses. XGBoost
was the optimal model both before and after applying SMOTE. Prior to SMOTE, the
model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test
accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed
importance to the duration between injury and gait analysis. Data patterns
showed early physiological compensations, followed by stabilization phases,
emphasizing prompt gait analysis. This study underscores the potential of
machine learning, particularly XGBoost, in gait analysis for orthopedic care.
Predicting post-injury complications, early gait assessment becomes vital,
revealing intervention points. The findings support a shift in orthopedics
towards a data-informed approach, enhancing patient outcomes.
