Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model

Menghan Liu; Xin Wei; Xiaodong Xing; Yunlong Cheng; Zicheng Ma; Jiwu Ren; Xiaofeng Gao; Ajing Xu

doi:10.1177/20552076241257456

Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model

Digit Health. 2024 May 24:10:20552076241257456. doi: 10.1177/20552076241257456. eCollection 2024 Jan-Dec.

Authors

Menghan Liu^{1

2}, Xin Wei¹, Xiaodong Xing¹, Yunlong Cheng³, Zicheng Ma⁴, Jiwu Ren⁴, Xiaofeng Gao³, Ajing Xu¹

Affiliations

¹ Department of Clinical Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
² School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
³ Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
⁴ Zhiyuan College, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Background and objective: Osteoporotic fractures significantly impact individuals's quality of life and exert substantial pressure on the social pension system. This study aims to develop prediction models for osteoporotic fracture and uncover potential risk factors based on Electronic Health Records (EHR).

Methods: Data of patients with osteoporosis were extracted from the EHR of Xinhua Hospital (July 2012-October 2017). Demographic and clinical features were used to develop prediction models based on 12 independent machine learning (ML) algorithms and 3 hybrid ML models. To facilitate a nuanced interpretation of the results, a comprehensive importance score was conceived, incorporating various perspectives to effectively discern and mine critical features from the data.

Results: A total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid model that synergistically combines the Support Vector Machine (SVM) and XGBoost algorithms demonstrated the best predictive performance in terms of accuracy and precision (above 90%) among all benchmark models. Blood Calcium, Alkaline phosphatase (ALP), C-reactive Protein (CRP), Apolipoprotein A/B ratio and High-density lipoprotein cholesterol (HDL-C) were statistically found to be associated with osteoporotic fracture.

Conclusions: The hybrid machine learning model can be a reliable tool for predicting the risk of fracture in patients with osteoporosis. It is expected to assist clinicians in identifying high-risk fracture patients and implementing early interventions.

Keywords: Fracture risk assessment; data mining; fracture prevention; machine learning; osteoporosis.