Research And Implementation Of Medical Rehabilitation Recommendation System Based On Hadoop Platform

With the continuous innovation of computer technology and internet technology,the medical rehabilitation information construction has gradually entered the networking and digitization stage.Medical rehabilitation system is one of the important ways to realize medical information,which can make it easier for patients to obtain medical rehabilitation information.Now it has received more and more attention.Aiming at the problems of large amount of data and poor accuracy in the current medical rehabilitation system for users to obtain medical rehabilitation information,this paper studies and implements a medical rehabilitation recommendation system based on hadoop.The system can recommend different medical rehabilitation programs for the user according to the user’s behavior data,thereby effectively improving the user’s rehabilitation effect.The main work of this article includes the following:1)Based on the analysis of the traditional recommendation technology,it comprehensively considers the characteristics of the user’s medical rehabilitation training behavior with implicit feedback data and explicit scoring data,and proposes a weight matrix decomposition model for medical rehabilitation information recommendation.In the process of optimizing the objective function(the square of the error between the predicted score and the actual score in the scoring matrix),the model adds the attribute features such as implicit feedback matrix,user attributes,and system usage to multi-weight normalized measurement.The improved objective function can better optimize the model parameters and provide users with more accurate prediction scores.2)For traditional data processing technology,there is a complex relationship between data processing and low data processing efficiency,and a medical healthcare architecture based on the Hadoop platform for hadoop index storage is designed.First,the medical information storage model based on the secondary index mechanism is designed in the HBase database.Secondly,the data is cleaned and processed through the MapReduce technology.Finally,the real-time calculation is performed using the Spark Streaming streaming computing framework,so that the system can not only store a large amount of data Medical data information,and effectively improve the efficiency of data processing.3)The hadoop-based medical rehabilitation recommendation system is designed and implemented.When the system is implemented,the SSM framework is used for design.Spring MVC controls and implements the business logic of the medical rehabilitation system.Mybatis is responsible for the persistence of the underlying relational database in the medical rehabilitation system.The system can not only collect and manage medical data information of a large number of users,but also can recommend suitable medical rehabilitation information for the user according to the user’s rehabilitation behavior data,and help the user to perform rehabilitation training more effectively.