title: Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.
abstract: OBJECTIVE
  A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools.
  
  
  METHODS
  We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS).
  
  
  RESULTS
  SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus.
  
  
  CONCLUSIONS
  Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
authors.affiliation: Department of Medical Engineering, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Korea. kdw@yuhs.ac.
authors.forename: Tae Keun
authors.initials: TK
authors.lastname: Yoo
authors.forename: Sung Kean
authors.initials: SK
authors.lastname: Kim
authors.forename: Deok Won
authors.initials: DW
authors.lastname: Kim
authors.forename: Joon Yul
authors.initials: JY
authors.lastname: Choi
authors.forename: Wan Hyung
authors.initials: WH
authors.lastname: Lee
authors.forename: Ein
authors.initials: E
authors.lastname: Oh
authors.forename: Eun-Cheol
authors.initials: EC
authors.lastname: Park
mesh_terms: D000368:Aged; D001185:Artificial Intelligence; D015519:Bone Density / Q000502:physiology*; D005260:Female; D006801:Humans; D008875:Middle Aged; D015663:Osteoporosis, Postmenopausal
keywords: Screening; clinical decision tools; machine learning; risk assessment; support vector machines