RETRACTED: A Hierarchical Visualization Fusion Method for Multi-information of Unconventional Mutation Medical Big Data

ABSTRACT Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269997. In the process of information fusion for medical data, there are some problems, such as low precision of data cleaning and more omission of duplicate data, which lead to the unsatisfactory effect of information fusion. This paper proposes a data collection strategy of a distributed network based on linear regression analysis, constructs a perceptual unconventional mutation data model by applying linear regression analysis method, and maintains the characteristics of perceptual data to realize data collection. The ID tags taken as data Transducer Identification of Strain gage are compared. Assuming that the TIDs of two data points are the same, that is, duplicate data are detected, the detected data set flows according to the method flow. To enhance the accuracy of data cleaning and avoid omission, the hierarchical visualization fusion method of radar maps is used to achieve multi-information fusion. It was found that the proposed method can clean the redundant data accurately and realize its efficient fusion.