Interactive Statistical Analysis Of Large Data Of Medical Information Based On Spark

At this stage,the health statistics work of the domestic health system has basically achieved full coverage of informationization.This provides the possibility for the statistics of data,as well as the timeliness and and effective the possibility of data statistics.But at the same time,we must also realize that the current medical information statistics technology is far from sharing the medical information big data.Big data technology is still in the early stage of medical information statistics.The medical and health management department faces huge medical information data,and there is no reliable technical means to grasp and analyze the data information.In the face of sudden health events,it cannot quickly provide reliable disposal methods.Doctors and researchers are faced with huge amounts of data,there is no reliable means to query valid information,and representative cases of diseases handled by individuals cannot be systematically counted.Therefore,how to conduct effective statistical analysis of massive medical data has important theoretical and academic value.This paper comprehensively uses literature analysis,comparative research,empirical analysis and other methods to comprehensively consider the existing big data processing technology,and determine the use of spark processing technology.With its memory-based computing,spark is suitable for machine learning iterative computing and has become the mainstream processing tool in the field of big data.Using a variety of application modules,it can be applied to various big data scenarios,and is also suitable for statistical analysis of medical information big data interaction in this study.Finally,an interactive statistical analysis of large data of medical information system is designed and verified by using appropriate technology.In the research process,the main functions of each layer are sorted out,the data processing flow is standardized,and the solution to optimize the data query efficiency is designed.The naive Bayes algorithm is used again to analyze the correlation between the patient detection parameters and related diseases.The medical data statistics platform based on domain mapping was carried out and the idea of platform testing was carried out.After empirical research,it proposes a solution to optimize the efficiency of data query.Based on the in-depth analysis of decision logic and business in the basic medical and health industry,it analyzes the effective methods of data information for medical auxiliary decision-making.The corresponding architecture and algorithm are designed in three aspects: processing and storage,efficient parallel computing service and visual display.Using the sampling data of the relevant data table involved in the outpatient process,the function and performance of the system were compared and tested to verifythe scientificity,effectiveness and reliability of the system.