Machine learning algorithm to predict fragility fractures and identification of important features – an explainable approach

Abstract In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites. We investigated seven ML algorithms (LASSO, Elastic Net, Random Forest, Decision Tree, Neural Network, XGBoost and Logistic Regression) using the data from the Canadian Multicentre Osteoporosis Study (CaMos) with participants aged 50 years or older. We considered 73 baseline features, including age, sex, menopause status, and bone mineral density (BMD), and the outcome was the first incidence of fracture at any of the following sites: hip, spine, pelvis, ribs, shoulder, and forearm, over a 19-year follow-up period. Data were divided into training (70%) and testing (30%) datasets. The ML algorithms were trained on the training dataset and evaluated on the test dataset in terms of the ROC_AUC. SHapley Additive exPlanations (SHAP) analysis was performed to identify the important features that contribute to the prediction of fracture, and to investigate the interaction among these features. In total, 7,753 subjects were included in the study. Approximately 72% were female, and the average age was 67 years. We found that the XGBoost algorithm had a slightly better ROC_AUC (0.70; 95% CI: 0.67, 0.73). From the SHAP analysis, we found that BMD was the most important feature that contributed to the prediction. The other important features include age, previous fracture, osteoporosis and menopausal status. Total hip BMD interacted the most with femoral neck BMD, lumbar spine BMD interacted the most with weight, previous fracture status interacted the most with femoral neck BMD, and age interacted the most with lumbar spine BMD. This study demonstrated that XGBoost was the most effective algorithm for predicting fragility fractures. In addition, we identified important features that contribute to the prediction of fragility fractures. Intervention focusing on these features will help to prevent the incidence of these fractures. Lay summaries We developed machine learning (ML) algorithms to predict fragility fractures, considering the incidence of fractures at different skeletal sites, including the hip, spine, pelvis, ribs, shoulder, or forearm, using 19 years of follow-up data from the Canadian Multicentre Osteoporosis Study (CaMos). We investigated seven ML algorithms and found that XGBoost had slightly better performance compared to other algorithms. We identified important factors that increase the risk of fractures, including BMD, age, and previous fracture. We also demonstrated how the interaction between these factors increases the risk of fractures. The intervention focusing on these factors will help to prevent fragility fractures.

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