Human health big data evaluation based on FPGA processor and big data decision algorithm - Sun, Kefeng; Yang, Dongjun (Microprocessors and microsystems, 2021)
At present, the development of health care industry is also very vigorous and prosperous, and has become one of the most widely developed industries in the world. Medical centers and service centers in various regions have begun to transform from medical model to health care model. This field programmable gate array has great advantages in this respect, and it is also one of the principles of patient-centered nursing. With the vigorous development of machine learning, its application scope is more and more extensive, and its application in medicine is also very common. People use machine learning to process big data in the medical field. In order to better manage patient data and realize patient-centered, we must analyze a large number of health data. The traditional management tools are not enough to support the analysis of modern data. Therefore, we should use advanced big data processing technology for relevant data processing, and use updated tools to meet the current medical needs. The signal processing based big data evaluation is to be done through FPGA. The proposed system contains three process these process are executed through the machine learning based. The first process preprocessing is used eliminate the noise of the image or irrelevant data avoided. The second process feature selection based decision tree technique used and then after the final process classification stage based machine learning technique is used to analysis of the big data accuracy level. FPGA based machine technique used to achieve the better result of the proposed system.