Mining MEDLINE for the Treatment of Osteoporosis

Yıldırım, Pınar; Çeken, Çınar; Hassanpour, Reza; Esmelioglu, Sadik; Tolun, Mehmet R.

doi:10.1007/s10916-011-9701-6

Cited by 4 publications

References 25 publications

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Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

Kılıç

Hosgormez

2015

J Med Syst

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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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Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

Kılıç

Hosgormez

2015

J Med Syst

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Predicting High-cost Patients in General Population Using Data Mining Techniques

Shenas¹,

Abdolmotalleb²

2012

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Risk Prediction Model for Osteoporosis Disease Based on a Reduced Set of Factors

Moudani

Shahin

Chakik

et al.

Recent Advances in Intelligent Technologies and Information Systems

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The health industry collects huge amounts of health data, which, unfortunately, are not mined to discover hidden information. Information technologies can provide alternative approaches to the diagnosis of the osteoporosis disease. In this chapter, the authors examine the potential use of classification techniques on a huge volume of healthcare data, particularly in anticipation of patients who may have osteoporosis disease through a set of potential risk factors. An innovative solution approach based on dynamic reduced sets of risk factors using the promising Rough Set theory is proposed. An experimentation of several classification techniques have been performed leading to rank the suitable techniques. The reduction of potential risk factors contributes to enumerate dynamically optimal subsets of the potential risk factors of high interest leading to reduce the complexity of the classification problems. The performance of the model is analyzed and evaluated based on a set of benchmark techniques.

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Mining MEDLINE for the Treatment of Osteoporosis

Cited by 4 publications

References 25 publications

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

Predicting High-cost Patients in General Population Using Data Mining Techniques

Risk Prediction Model for Osteoporosis Disease Based on a Reduced Set of Factors

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