Processing And Analysis Of Human Physical Data Based On Association Algorithm

As one of the hot research branches in the field of data mining,association analysis techniques is that finds useful relationships from a large number of data items with potentially unknown relationships,Hypertension is the common chronic disease that seriously endangers human health.In recent years,its prevalence and mortality have always been rising globally.With the development of the medical information industry,the more and more data on chronic diseases such as hypertension are stored in medical databases.How to find useful information from these historical medical data to provide scientific and accurate basis for future disease diagnosis and pathological research has become an urgent problem to be solved.Compared with traditional data analysis methods,data mining technology can effectively solve this problem.In this topic,the association analysis techniques is used to explore the potential relationship between the physiological parameters of patients with chronic hypertension and the risk level of hypertension from a large number of clinical hypertension patients data.This article focuses on the following tasks:(1)The related theories of data mining technology and association analysis technology are expounded.The application of association analysis technology in medical field is analyzed.The Apriori algorithm in association analysis is mainly studied.The shortcomings of Apriori algorithm are analyzed,and the current some general optimization methods are introduced to offer new thoughts of the subsequent optimization.(2)The improved Apriori algorithm combines the subject research content introduction constraint to filter the transaction database D to obtain the transaction database D1,and then finds the frequent item set in the transaction database D1.In order to avoid the Apriori algorithm repeatedly scanning the database and taking time-consuming shortcomings,the data mapping method is adopted,so that the time overhead can be reduced.Finally,the performances of the Apriori algorithm before and after the improvement are tested in term of the experimental data set,the superiority of the improved algorithm performance is verified.(3)The improved Apriori algorithm was applied in the hypertension data sets collected in this topic for the association analysis.The measurement level of relevance coefficients will be introduced to evaluate rules,and the association rules will be explained in detail,which conform to medical disciplines.Based on the above rules,the risk level of the hypertensive patients can be accurately judged,the auxiliary reference is helpful for the doctor diagnosis,and the daily prevention instructions can be provided for the patient.(4)The application software that designs and implements the association rules can not only evaluate the physical parameters submitted by the user,but also classify and save the submitted data,combine the auxiliary functions,improve the design implementation,and provide the new way to the application of the association rules.