A study on the development of a machine learning-based clinical prediction model for bone health management in breast cancer patients

Background Breast cancer is among the most prevalent malignant neoplasms afflicting women globally, with its treatment and disease itself exerting a substantial influence on bone health. Given the prolonged survival rates of breast cancer patients, the management of bone health issues has become a critical component of comprehensive cancer care. The objective of this study was to develop clinical predictive models using machine learning methods and apply these models to the management of bone health in breast cancer patients. Methods This was a multicenter retrospective cohort study. We included 139 breast cancer patients who were diagnosed from March 2022 to December 2024 in three hospitals in China. We developed predictive models with optimal features by using algorithms such as random forest (RF), K nearest neighbors (KNN), support vector machines (SVM), and extreme gradient boosting (XGB) and determined and assessed the machine learning algorithm with the highest accuracy rate for breast cancer-related bone loss on the basis of the area under curve (AUC) of the subjects. Results A total of 139 study participants were included in this study, including 78 patients with osteopenia (including osteoporosis, T≤-1.0) and 61 patients with normal bone mass (T>-1.0). Specific indicators of bone loss were identified, and seven models were constructed: logistic regression (LR), the bagging tree algorithm (BT), RF, adaptive boosting (AdaBoost), XGBoost, KNN, and SVM, among which the AdaBoost prediction model performed the best in predicting breast cancer-related bone loss, with the best performance AUC of 0.995. The model can be generated into a publicly accessible applet. Conclusion The data was incorporated into seven machine learning algorithms and models were constructed, which were then compared to arrive at the optimal model.The AdaBoost model was selected as the final model, which can predict breast cancer-related bone loss by collecting patient information through a simple question-and-answer session and importing laboratory blood tests and reports of examinations such as bone mineral density; this model is expected to guide the management of clinical bone health in patients with breast cancer and improve the prognosis of patients. Trial registration Ethics Approval and Consent to Participate This study was approved by the Institutional Review Board of the Seventh People's Hospital Affiliated with Shanghai University of Traditional Chinese Medicine. Approval number: ChiCTR2200057785(17th March 2022).