A data mining approach to study risk factors of hyperuricemia

摘要

Today, 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 data in the source and data complexity are some of the difficulties in medical data research. This research work proposes a way of dealing with big medical data with a case study of gout patients 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.