Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Abstract

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Key words： XGBoost; deep neural network; healthcare; risk prediction

Acknowledgements

This work was partially supported by the National Key R&D Program of China (Nos. 2020YFC2005502 and 2018YFB1004700), the National Natural Science Foundation of China (Nos. 61872238 and 61972254), the Science and Technology Commission of Shanghai Municipality (No. 19401900500), the Innovation Program of Shanghai Health Commission (Nos. 201840121 and ZY(2018-2020)-ZWB-1001-CPJS1), the CCF-Huawei Database System Innovation Research Plan (No. CCF-Huawei DBIR2019002A), and the program of Hospital Clinical Research, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (No. 19XHCR11C).

Compliance with ethics guidelines

Yaxin Chen, Tianyi Yang, Xiaofeng Gao, and Ajing Xu declare that they have no conflict of interest. All procedures followed complied with the ethical standards of the responsible committee on human experimentation (institutional and national) and the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients included in the study.

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