A Novel Fracture Prediction Model Using Machine Learning in a Community‐Based Cohort

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.

CatBoost for big data: an interdisciplinary review

“…“A novel fracture prediction model using machine learning in a community-based cohort”, by Kong et al is a study on using ML on the task of predicting fragility fractures in patients [ 24 ]. Fragility fractures are bone fractures that occur from little or no trauma.…”

“…However, we choose to include their study in our survey because it shows how CatBoost can outperform other ML algorithms in tasks involving heterogeneous, categorical data. Assuming the list of clinical characteristics of participants listed in [ 24 ] [Tab. 1] is the comprehensive list of features, there are 35 features in the dataset, 9 of which we consider to be categorical.…”

CatBoost for Big Data: an Interdisciplinary Review

“…In these studies related to medicine, we find one study that supports the idea that CatBoost is a good choice to use when data is heterogeneous and categorical. That study is Kong et al [52], where the results for predicting fragility fractures show CatBoost yields the best performance. One reason for this may be that the dataset is heterogeneous data from surveys that cohort members submit for the study.…”

“…However, we choose to include their study in our survey because it shows how CatBoost can outperform other ML algorithms in tasks involving heterogeneous, categorical data. Assuming the list of clinical characteristics of participants listed in [52,Tab. 1] is the comprehensive list of features, there are 35 features in the dataset, 9 of which we consider to be categorical.…”

“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [49], Finance [24] [92] [91] [89], Medicine [87] [52] [4] [68], Biology [51] [55], Electrical Utilities Fraud [20] [66] [36], Meteorology [44] [29], Psychology [71] [3], Traffic Engineering [85] [76], Cyber-security [6], Bio-chemistry [88] [58], and Marketing [50]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”