Machine learning-based prediction of pediatric scoliosis using foot pressure and body angles

This study aims to analyze the key features of scoliosis based on children's foot pressure and body angle data and to develop a predictive model to support early screening for scoliosis in pediatric populations.

Data were collected from 5970 primary school students in Kunming. After excluding incomplete and anomalous samples, 3723 students were included. Machine learning algorithms, including Logistic Regression(LR), Support Vector Machine(SVM), Random Forest(RF), and LightGBM, were employed to construct classification models. Model performance was evaluated using accuracy, ROC curves, AUC, and other metrics. The optimal model was selected based on model performance comparisons, and SHAP (Shapley Additive exPlanations) interpretability analysis was conducted.

Statistical analysis revealed significant differences between children with scoliosis and healthy controls in toe out angle and Center of Pressure (COP) sway area. The LightGBM model outperformed others with an AUC of 0.971, significantly higher than LR (0.566), SVM (0.723), and RF (0.899). The LightGBM model also reached an accuracy of 92%. SHAP analysis identified that the toe out angle and COP sway area were the most important features for scoliosis prediction. This finding is consistent with the two sets of differential features identified in the statistical analysis.

This study developed a predictive model for pediatric scoliosis using foot pressure and body posture data, demonstrating high accuracy and efficiency in predicting scoliosis. The study further confirmed the key role of foot pressure distribution and body posture features in scoliosis prediction, providing a new technical approach and reference for early screening of scoliosis.