Development and evaluation of a machine learning based risk prediction model for osteoporosis

Abstract

Osteoporosis is a systemic bone disease that can lead to decreased bone strength and increased susceptibility to fractures. This study aims to evaluate the level of cytokines and other common blood biomarkers, and establish a machine learning-based risk prediction model for osteoporosis patients. The clinical data of patients with osteoporosis who were hospitalized in the Seventh Medical Center of the Chinese People’s Liberation Army General Hospital from January 2023 to September 2024 were retrospectively collected. 16 cytokines and other potential predictive variables were analyzed to determine the independent risk factors. Eleven machine learning models were established, and the performance of the models was evaluated using the ROC curve, the PR curve and the confusion matrix, and the optimal model was selected. SHAP to provide insights into the model’s predictions and construct a visual representation of the prediction model. A total of 187 patients were included, and 16 characteristics such as IL-8 were identified as risk characteristics. By comparing 11 different models, it was found that the Gradient Boost was the best model, with an AUC value of 0.91, an accuracy rate of 0.81, an F1 score of 0.80, and a relatively balanced specificity and sensitivity. And the SHAP value was used to reveal the direction and strength of each feature in predicting osteoporosis. This study successfully developed a risk prediction model based on 16 common variables, including IL-8, providing critical evidence for the early identification and prevention of osteoporosis patients, with potential clinical significance.