Predicting Fracture Risk for Chinese Elderly Osteoporosis Patients by Hybrid Model

Abstract

Abstract Background: In China, there are many elderly people who suffer from osteoporosis. Fractures, the most serious complication of osteoporosis, can greatly affect people’s quality of life and add pressure to the social pension system. Thus, predicting fracture risk in elderly patients with osteoporosis is imperative, which allows for timely treatment and ameliorates the strain on healthcare resources. Machine learning (ML) models based on Electronic Health Records (EHR) can be contracted to predict fracture risk. Methods: Data of patients with osteoporosis were extracted from the Electronic Health Records (EHR) in Xinhua Hospital. Demographic and clinical features that are readily available from EHR were used to develop prediction models based on 12 independent ML algorithms (Naïve Bayes classifier [BP], logistic regression [LR], decision tree [DT], k-nearest neighbors [KNN], support vector machine [SVM], random forests [RF], extremely randomized trees [ERT], gradient-boosted decision trees [GBDT], adaptive boosting [AdaBoost], categorical boosting [CatBoost], extreme gradient boosting [XGBoost], multilayer perceptron [MLP]) and 3 hybrid ML models (XGBoost + MLP, XGBoost + LR, XGBoost + SVM). A comprehensive importance score was designed to interpret features from several aspects. Results: A total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid XGBoost and SVM model had the best predictive performance in terms of accuracy and precision (above 90%) among all models. We obtained 20 important features according to the comprehensive importance score, which represents high-risk factors for fractures and was interpreted from a clinical point of view. Conclusions: The hybrid XGBoost and SVM model can be reliable tools for predicting the risk of fracture in patients with osteoporosis. And the hybrid model had the best predictive performance, which can be used to assist clinicians in identifying high-risk fracture patients and implementing early interventions.