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.
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Acknowledgments
This work was partially supported by The Research Fund of Istanbul University. Project Number: UDP-7098 and YADOP-36785. We would like to extend our appreciation to all medical staff who participated in this study for their invaluable support, especially Dr. Sait Sager, MD
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This article is part of the Topical Collection on Systems-Level Quality Improvement.
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Kilic, N., Hosgormez, E. Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches. J Med Syst 40, 61 (2016). https://doi.org/10.1007/s10916-015-0413-1
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DOI: https://doi.org/10.1007/s10916-015-0413-1