title: Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.
abstract: Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.
authors.affiliation: Engineering Faculty, Electrical and Electronics Department, Istanbul University, 34320, Avcilar, Istanbul, Turkey. niyazik@istanbul.edu.tr.
authors.forename: Niyazi
authors.initials: N
authors.lastname: Kilic
authors.affiliation: Biomedical Engineering Department, Institute of Sciences, Istanbul University, Beyazit, Istanbul, Turkey.
authors.forename: Erkan
authors.initials: E
authors.lastname: Hosgormez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015519:Bone Density / Q000502:physiology; D003720:Densitometry / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D010024:Osteoporosis / Q000175:diagnosis*; D058866:Osteoporotic Fractures / Q000517:prevention & control*; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results
keywords: Bone mineral density; Ensemble learning classification; IBk; Osteoporosis; Random forest; T-score