<![CDATA[The information technology may provide alternative approaches to Osteoporosis disease diagnosis. This systematicreview was performed to compare the diagnostic accuracy of vertebral fracture assessment. In this study, we examinethe potential use of classification techniques on a massive volume of healthcare data, particularly in prediction ofpatients that may have Osteoporosis through its risk factors. For this purpose, we propose to develop a new solutionapproach based on Random Forest decision tree to identify the osteoporosis cases. There has been no researchin using the afore-mentioned algorithm for Osteoporosis patientsââ¬Ë prediction. The reduction of the attributes consiststo enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree ofcomplexity. A computer-aided system is developed for this purpose. The performance of the proposed model in thisstudy is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.]]>
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