A Novel Fracture Prediction Model Using Machine Learning in a Community‐Based Cohort. (10th February 2020)

• Record Type: Journal Article
• Authors: Kong, Sung Hye; Ahn, Daehwan; Kim, Buomsoo (Raymond); Srinivasan, Karthik; Ram, Sudha; Kim, Hana; Hong, A Ram; Kim, Jung Hee; Cho, Nam H; Shin, Chan Soo
• Is Part Of: JBMR plus. Volume 4:Number 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-10
• Subjects: FRACTURE -- MACHINE LEARNING -- PREDICTION MODEL -- PROSPECTIVE COHORT; Bones -- Diseases -- Periodicals; Bones -- Metabolism -- Periodicals; Orthopedics -- Periodicals; 612.75104
• DOI: 10.1002/jbm4.10337
• Languages: English
• ISSNs: 2473-4039

Abstract

ABSTRACT: The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine‐learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed to develop a novel fracture prediction model using machine‐learning methods in a prospective community‐based cohort. In this study, 2227 participants (1257 females) with a baseline bone mineral density (BMD) and trabecular bone score were enrolled from the Ansung cohort. The primary endpoint was the fragility fractures reported by patients or confirmed by X‐rays. We used 3 different models: CatBoost, support vector machine (SVM), and logistic regression. During a mean 7.5‐year follow‐up (range, 2.5 to 10 years), fragility fractures occurred in 537 (25.6%) of participants. In predicting total fragility fractures, the area under the curve (AUC) values of the CatBoost, SVM, and logistic regression models were 0.688, 0.500, and 0.614, respectively. The AUC value of CatBoost was significantly better than that of FRAX (0.663; p < 0.001), whereas the the SVM and logistic regression models were not. Compared with the conventional models such as SVM and logistic regression, the CatBoost model had the best performance in predicting total fragility fractures ( p < 0.001). According to feature importance in the CatBoost model, the top predicting factors (listed in order) were total hip, lumbar spine, and femur neck BMD, subjective arthralgia score, serum creatinine, and homocysteine. The latter three factors were listed higher than conventional predictors such as age or previous fracture history. In summary, we hereby report the development of a prediction model for fragility fractures using a machine‐learning method, CatBoost, which outperforms the FRAX model as well as two conventional machine‐learning models. The model was also able to propose novel high‐ranking predictors. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.