Design And Implementation Of Intelligent Health Service Platform Based On Data Mining

In recent years,more and more residents get chronic disease,such as hypertension.Thus,a lot of health service platforms come into the family.However,these platforms have some disadvantages.First,they can only do some simple measurements and they are unable to give the future of health to user.Second,these platforms are unable to analyze data collected by devices and unable to dig out useful information.Third,the measuring of chronic diseases will produce vast amounts of data and most of these platforms will drop a lot of potential value of data to save storage costs.To cure the above problems,this thesis designs the intelligent health service platform based on data mining.This platform consists of data acquisition module,data storage module,data mining module and data display module.The data acquisition model can acquire data from health device by bluetooth.The data storage module can store data by RDBMS and cloud storage.The data mining module can dig out useful knowledge by techniques of data mining,this module uses improved k-means algorithm to analyze data processed combining MapReduce framework and uses gray prediction algorithm to predict the future health of user.The data display module use technologies of JavaEE to build web platform based on hadoop and the analysis results are displayed to the user by diagrams.Last,this thesis implements this platform.This platform achieves the requirements of design index after testing and it can provide services to users,such as health analysis and early warning.This platform is easy to use and predict correctly,so it has very high use value and wide application prospects.