title: Machine Learning Principles Can Improve Hip Fracture Prediction.
abstract: Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis ("bagFDA") performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Naïve Bayes adjustments. A "bagFDA" model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting ("xgbTree") performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an "xgbTree" model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.
authors.affiliation: Department of Endocrinology, Aalborg University Hospital, Moelleparkvej 4, 9000, Aalborg, Denmark. ckruse@dcm.aau.dk.|Department of Clinical Medicine, Aalborg University, Sdr. Skovvej 15, 9000, Aalborg, Denmark. ckruse@dcm.aau.dk.|Department of Endocrinology, Aalborg University Hospital, Hobrovej 19, 9100, Aalborg, Denmark. ckruse@dcm.aau.dk.
authors.forename: Christian
authors.identifier: 0000-0001-5590-2245
authors.initials: C
authors.lastname: Kruse
authors.affiliation: Department of Cardiology, Nephrology and Endocrinology, Nordsjaellands Hospital, Dyrehavevej 29, 3400, Hilleroed, Denmark.|Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark.
authors.forename: Pia
authors.initials: P
authors.lastname: Eiken
authors.affiliation: Department of Endocrinology, Aalborg University Hospital, Moelleparkvej 4, 9000, Aalborg, Denmark.|Department of Clinical Medicine, Aalborg University, Sdr. Skovvej 15, 9000, Aalborg, Denmark.
authors.forename: Peter
authors.initials: P
authors.lastname: Vestergaard
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods; D000368:Aged; D015519:Bone Density / Q000502:physiology*; D015331:Cohort Studies; D005260:Female; D006620:Hip Fractures / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D058866:Osteoporotic Fractures / Q000175:diagnosis* / Q000503:physiopathology; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
keywords: FRAX; Fracture; Machine learning; Osteoporosis; Prediction