Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ensemble learning methods are one of the most porful tools for the pattern classification problems. , the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection re investigated. Six feature set models re 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) re used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients re classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure re 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.
['Niyazi Kilic', 'Erkan Hosgormez']
I am writing a research paper on a idea Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ensemble learning methods are one of the most porful tools for the pattern classification problems. , the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection re investigated. Six feature set models re 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) re used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients re classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure re 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."}]