Analysis And Research Application Of Hyperthyroidism Disease Model Based On Medical Big Data

With the vigorous development of medical informatization and big data technology,massive amounts of big data have been produced,and the big data is no longer simply "big" on the scale of data,but also has the characteristics of large amount of data,diversified forms,variable speed and high value.How to use medical big data mining technology to deeply mine and analyze huge data sets,and how to visualize the hidden values mined,so as to provide theoretical guidance for medical staff quickly and efficiently,has become an urgent problem for researchers,and has become the key goal of medical information construction and medical service level development.This paper studies and optimizes the shortcomings of the Apriori algorithm,and proposes the CM＿HI＿p Apriori algorithm on the basis of the NCM＿Apriori＿1 algorithm.The parallel operation of CM＿HI＿p Apriori algorithm is realized by Map Reduce operation framework on Hadoop platform,and the performance of the algorithm is verified by experimental comparison and analysis,and the algorithm is applied to big data mining of hyperthyroidism diseases.The main research contents are as follows:First,conduct a comparative experimental study of NCM＿Apriori＿1algorithm and Apriori algorithm.According to the nature theorem,thought and process of the NCM＿Apriori＿1 algorithm,this algorithm is designed and implemented on the Map Reduce framework,and compared with the traditional Apriori algorithm,the effectiveness of the NCM＿Apriori＿1algorithm is verified.Second,the improvement and optimization of the NCM＿Apriori＿1algorithm.Analyze the core idea of the NCM＿Apriori＿1 algorithm,and combine the shortcomings of the traditional Apriori algorithm,and propose three improvement strategies: 1)parallel processing of data partitioning,2)storage and query of frequent itemsets based on the Hash Map structure,3)increase the evaluation criteria of interest;and proposed the CM＿HI＿p Apriori algorithm,and transplanted the CM＿HI＿p Apriori algorithm to the Hadoop for Map Reduce design and implementation.Through the comparison of the running time of the two algorithms and the comparative analysis of the performance of the CM＿HI＿p Apriori algorithm itself;the correctness,efficiency,stability and good scalability of the CM＿HI＿p Apriori algorithm are verified.Finally,the CM＿HI＿p Apriori algorithm is used in the mining and application of hyperthyroidism medical big data.Through the calculation and analysis of many experiments,the most suitable mining parameter are obtained in this experiment,and the set parameter are used to mine the positive and negative strong association rules that users are interested in,and explain and further preventive suggestions.And design a hyperthyroid disease medical big data mining system for users to use and professional analysis.