Clinical Application of a Big Data Machine Learning Analysis Model for Osteoporotic Fracture Risk Assessment Built on Multicenter Clinical Data in Qingdao City.

Background: Osteoporotic fractures (OPF) pose a public health issue, imposing significant burdens on families and societies worldwide. Currently, there is a lack of comprehensive and validated risk assessment models for OPF. This study aims to develop a model to assess and predict the risk of OPF in Qingdao City, China.

Methods: From January 2021 to January 2023, we recruited 84 osteoporotic patients diagnosed with fractures from Qingdao University Affiliated Hospital, Qingdao Municipal Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, and Qingdao Central Hospital as the experimental group. In addition, 112 osteoporotic patients without fractures were recruited as the control group. In this study, we employed seven machine learning models, namely Adaboost, random forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Gradient Boosting Decision Trees (GBDT), to analyze the risk factors influencing the occurrence of OPF. Next, we plotted receiver operating characteristic (ROC), Precision-Recall (PR), and calibration curves to evaluate the predictive values of the different risk assessment models for OPF.

Results: Among the seven models built based on the training set data, the Adaboost model showed area under the curve (AUC), sensitivity, and specificity values close to 1, indicating the best classification performance. In the test set, the AUC values for the RF, SVM, LR, KNN, NB, AdaBoost, and GBDT models were 0.936, 0.905, 0.88, 0.93, 0.862, 0.939, and 0.859, respectively (p < 0.001). All sensitivity and specificity values for these models were higher than 0.8, with sensitivity and specificity values of the Adaboost model closest to 1. Additionally, six models had an area under the Precision-Recall curve (prAUC) values higher than 0.9, except KNN at 0.284 (p < 0.001). The calibration curves of the seven models did not significantly deviate from the ideal curve, indicating acceptable discriminative ability and predictive performance of the predictive model. All results showed that trabecular bone score (TBS) was the most important variable affecting the model, followed by osteocalcin (OST) and hunchback.

Conclusions: Given the various clinical data from patients with OPF, we assessed and demonstrated the good predictive performance of our risk predictive models. This model will enable us to take timely intervention measures to reduce the incidence of OPF and improve patient prognosis.