A case study of medical big data processing: Data mining for the hyperuricemia
Junyan Tan, Tianyu Xiong, Hongxia Miao, Rurong Sun, Min Jie Wu · 2018
Today, the world comes into a booming information age. With the rapid development of computer and sensors, various bits of data are continuously being generated as time goes by. The amount of data to be processed has entered into the big data category. Data mining is widely used to discover hidden information in the large amounts of data. However, data mining applied to medical databases is a challenging process. The unavailability of large raw data and data complexity are some of the difficulties encountered. This research work proposes a way of dealing with big medical data with a case study of gout disease. Gout is common chronic disease caused by the most important risk factor hyperuricemia. There is no drug to completely cure the gout, the patient is suffering a lot of pain. It is important to control the occurrence of gout and to study the association of gout with other metabolic diseases. This paper discusses methods for the analysis of this complex dataset of this disease, to help get more understanding of the disease and associated diseases. Association Rule is used as the mining algorithm for the data processing. An associated relation is proposed according to the experiments, which applies auxiliary support for doctor's clinical diagnosis and disease research in the local.