Data Correlation Analysis Of SLE Nephropathy

Systemic lupus erythenhatosus nephritis(LN)refers to the clinical manifestations of renal disease and/or abnormal renal function based on systemic lupus erythenlatosus(SLE).Systemic lupus erythematosus nephritis is a disease with multiple complications and the condition is complex and diverse.So far,the causes and pathogenesis of LN have not been fully elucidated.There are a large number of patients with LN in China,and there are many medical data stored in LN patients in hospitals.However,the value of these data is not fully utilized to dig out knowledge about the treatment of LN.How to use data mining technology to obtain valuable information from LN medical data and provide advice and help for doctors' decision-making is the focus of this study.Data mining technology is the key to discovering the knowledge behind LN medical data.Therefore,we must first study the theoretical knowledge of data mining,determine the data mining process,and focus on the principal component analysis algorithm,K-Means clustering algorithm and Apriori association rule algorithm.Then,the data is preprocessed by principal component analysis algorithm and K-Means clustering algorithm.The main influence index of LN is excavated through the data dimension reduction by principal component analysis algorithm.Then,LN medical data is preprocessed with data to form modeling data,and the data is brought into the Apriori algorithm model.Data mining is performed on the main indicators that affect LN,and the correlation between the indicators is obtained,and the correlation between the indicators is established.rule.Finally,the results of the association rules are filtered and analyzed.Then the results of the rules are applied to the LN for explanation.The diagnosis rules that are consistent with the medical knowledge are obtained to provide doctors with suggestions and help in the diagnosis and treatment of LN.