Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Abstract

Big Data-Driven Fabric Future Systems (BDD-FFS) is currently attracting widespread attention in the healthcare research community. Medical devices rely primarily on the intelligent Internet to gather important health-related information. According to this, we provide patients with deeply supportive data to help them through their recovery. However, due to the large number of medical devices, the address of the device can be modified by intruders, which can be life-threatening for serious patients (such as tumor patients). A large number of abnormal cells in the brain can lead to brain tumors, which harm brain tissue and can be life-threatening. Recognition of brain tumors at the beginning of the process is significant for their detection, prediction and therapy. The traditional approach for detecting is for a human to perform a biopsy and examine CT scans or magnetic resonance imaging (MRI), which is cumbersome, unrealistic for great amounts of resource, and requests the radiologist to make inferential computations. A variety of automation schemes have been designed to address these challenges. However, there is an urgent need to develop a technology that will detect brain tumors with remarkable accuracy in a much shorter time. In addition, the selection of feature sets for prediction is crucial to realize significant accuracy. This work utilizes an associative action learner with an advanced feature group, Partial Tree (PART-T), to detect brain tumor recognition grades. The model presented was compared with existing methods through 10-fold cross-validation. Experimental results show that partial trees with advanced feature sets are superior to existing techniques in terms of performance indicators used for evaluation, such as accuracy, recall rate and F-measure.