Classification Algorithms For Predicting The Risk of Osteoporotic Fracture
10.15520/jmbas.v6i8.133
Resumo
The information technology may provide alternative approaches to Osteoporosis disease diagnosis. This systematic
review was performed to compare the diagnostic accuracy of vertebral fracture assessment. In this study, we examine
the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of
patients that may have Osteoporosis through its risk factors. For this purpose, we propose to develop a new solution
approach based on Random Forest decision tree to identify the osteoporosis cases. There has been no research
in using the afore-mentioned algorithm for Osteoporosis patients‘ prediction. The reduction of the attributes consists
to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of
complexity. A computer-aided system is developed for this purpose. The performance of the proposed model in this
study is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.
How to Cite
Hyunjo Kim (2018). Classification Algorithms For Predicting The Risk of Osteoporotic Fracture. Journal of Medical Biomedical and Applied Sciences , v. 6 n. 8 (2018) , 96-101 . https://doi.org/10.15520/jmbas.v6i8.133