Abstract
Vertebral tumors have a percentage of back pain that causes other vertebral region-born symptoms. Cancers that affect the vertebral column are visceral organ cancer metastases that are mostly seen in older patients. Vertebral dysfunction and neurological failure vertebral column cancers are the most important occurred cancers for patients. In the past, only few methods have been used to combat main and metastatic vertebral tumors. These methods are accessible for short-term monitoring and possess standardized classification consistency for vertebral diagnosis. In this paper, geometric rough propagation neural network has been used for the identification of genetic factors in the examination of a clinical sample with vertebral columns. The proposed neural network has C-statistics of 79.1%, a parameter pitch of 96.1%, and configuration for measurement in the study range with the Brier's score of 95.6%. The algorithm shows great net gain on the decision curve study, with promising performance results of 98.5% on internal testing for preoperative non-routine estimation of discharges with 0.5% error rate and 96% accuracy range. Also, these models have been externally validated by the online healthcare careers cloud-based open access web application on Internet of Medical Things Platform with 97.9% specificity ratio.