Using Explainable Machine Learning to Identify Determinants of Spinal Deformities in Children: It's Not Only About What, but Also About How.

BACKGROUND

Spinal deformities in children represent a relevant public health issue, with possible long-term consequences. Timely identification of their determinants is essential for adequate prevention.

METHODS

This study was a secondary analysis of data from the 2019 Serbian National Health Survey, including 1309 children aged 5-14 years. Logistic regression with LASSO regularization and multiple ML algorithms were tested, with XGBoost selected as the optimal model. Class imbalance was addressed using class weighting and SMOTE. Model interpretability was achieved using SHAP analysis.

RESULTS

The prevalence of spinal deformities was 8.6%. Univariable analyses showed that age, poorer self-rated health, chronic illness, recent injuries, and pes planus were significantly associated with spinal deformities. Family-related variables showed no significant associations. Among the evaluated models, XGBoost demonstrated the most stable performance across the applied evaluation metrics and the best balance between predictive performance and interpretability. SHapley Additive exPlanations (SHAP) analysis showed that pes planus was the strongest determinant, followed by age and chronic illness, while socio-demographic and family factors had minimal influence.

CONCLUSION

Explainable machine learning models, particularly XGBoost combined with SHAP, can allow for the identification and interpretation of key determinants of spinal deformities in children. Pes planus was shown to be modifiable and relevant associated determinant, supporting its importance in early screening and preventive strategies.