Improving prediction of fragility fractures in postmenopausal women using random forest.
Authors
Affiliations (8)
Affiliations (8)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, Cuenca, 16071, Spain; Medical Analysis Expert Group, Castilla-La Mancha Institute of Health Research (IDISCAM), Toledo, 45071, Spain. Electronic address: [email protected].
- Department of Cell Biology, Faculty of Medicine. IOBA-Eye Institute, University of Valladolid, Valladolid, 47005, Spain.
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, Cuenca, 16071, Spain; Medical Analysis Expert Group, Castilla-La Mancha Institute of Health Research (IDISCAM), Toledo, 45071, Spain.
- Department of Gynecology, Ramón y Cajal University Hospital, Madrid, 28034, Spain.
- Department of Internal Medicine, Río Hortega University Hospital, Valladolid, 47012, Spain.
- Department of Internal Medicine, Río Carrión General Hospital, 34005, Palencia, Spain.
- Department of Medicine and Psychiatry, University of Cantabria, 39005 Santander, Spain; Department of Internal Medicine, Hospital Marqués de Valdecilla-IDIVAL, 39008 Santander, Spain.
- Department of Internal Medicine, Río Hortega University Hospital, Valladolid, 47012, Spain; Department of Medicine, Faculty of Medicine, University of Valladolid, Valladolid, 47005, Spain.
Abstract
Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a major source of morbidity and mortality among postmenopausal women. This condition poses both clinical and societal challenges, as its consequences include a significant reduction in quality of life, prolonged dependency, and a substantial increase in healthcare costs. Therefore, the development of reliable tools for predicting fracture risk is essential for the effective management of affected patients. In this study, we developed a predictive model based on the Random Forest (RF) algorithm for risk stratification of fragility fractures, integrating clinical, demographic, and imaging variables derived from dual-energy X-ray absorptiometry (DXA) and 3D modeling. Two independent cohorts were analyzed: the HURH cohort and the Camargo cohort, enabling both internal and external validation of the model. The results showed that the RF model consistently outperformed other classification algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), decision trees (DT), and Gaussian naive Bayes (GNB), demonstrating high accuracy, sensitivity, specificity, area under the ROC curve (AUC), and Matthews correlation coefficient (MCC). Additionally, variable importance analysis highlighted that previous fracture history, parathyroid hormone (PTH) levels, and lumbar spine T-score, along with other densitometric parameters, were key predictors of fracture risk. These findings suggest that the integration of advanced machine learning techniques with clinical and imaging data can optimize early identification of high-risk patients, enabling personalized preventive strategies and improving the clinical management of osteoporosis.