Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.

PURPOSE

To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.

METHODS

This retrospective cohort study examined 718 patients with hip fractures hospitalized at the Daejeon Eulji Medical Center between January 2020 and June 2022. Demographic and clinical variables, and rehospitalization data were collected at 6 weeks and 3, 6, 12, and 24 months. Cox proportional hazards (CoxPH), random survival forest (RSF), gradient boosting (GB), and fast survival support vector machine (SVM) models were developed. Model performance was assessed using the concordance index (c-index), area under the curve (AUC), and Kaplan-Meier survival curves. Feature importance was analyzed using permutation importance, with the best model selected based on overall performance.

RESULTS

Hyperparameter tuning optimized the models. The GB model had the highest mean AUC of 0.868, followed by the RSF (0.785), SVM (0.763), and CoxPH (0.736) models. Feature importance analysis highlighted femoral neck T-score, age, body mass index, operation time, compression fracture, and total calcium as significant predictors. Feature selection improved the c-index for the RSF model from 0.742 to 0.874 and CoxPH model from 0.717 to 0.915; the GB and SVM models exhibited a c-index decline post-feature selection. The GB and RSF models predicted lower rehospitalization probabilities than Kaplan-Meier estimates; the CoxPH model's predictions were closely aligned with the observed data.

CONCLUSIONS

The effect of feature selection on model performance highlights the need for comprehensive variable selection and model evaluation strategies to improve predictive accuracy.