| With the aging of China’s society,incidence rate of pelvic floor functional disorders will show an upward trend.Pelvic floor dysfunction diseases have become one of the five most common chronic diseases.Their symptoms seriously affect the quality of life and physical and mental health of adult women.It is necessary to further improve adult women’s cognition of pelvic floor dysfunction and achieve early intervention and treatment of pelvic floor dysfunction.The important link of early intervention and early treatment for pelvic floor dysfunction is to diagnose and screen pelvic floor diseases,and how to make use of the data that is easy to get for the initial screening of the patients is of great significance for improving the screening methods,improving the screening efficiency,reducing the severe incidence rate of pelvic floor diseases and improving the quality of life of patients.The prediction system of female pelvic floor disease was designed and implemented.Firstly,through the analysis of user requirements,the system has carried on the detailed architecture design,function design and database design.The functions of the female pelvic floor disease prediction system include data entry function,data management function,system management function,data integration function and data analysis function.Secondly,the prediction system of female pelvic floor disease was implemented.The data entry module is mainly used to input the basic data,body data and pelvic floor data of patients;Data management module is used to export,query and statistics the patient’s medical record information;The system management module is mainly used to manage the basic information of users in the system,including user management and hospital management;The data integration module is mainly used to integrate the basic information,body data or other pelvic floor data of patients;The data analysis module is mainly used to analyze the correlation between body data and pelvic floor diseases,and to predict patients’ pelvic floor diseases.The implementation of the module mainly uses the gradient lifting decision tree algorithm in ensemble learning to train the data set formed in the data integration module,and uses the trained gradient lifting tree model to predict pelvic floor diseases.Finally,the prediction system of pelvic floor disease in women was tested and analyzed.The results of test and operation showed that the prediction system of pelvic floor disease in women had good performance in both functional and non-functional aspects.The data analysis module can predict the related pelvic floor diseases by the body index data of the subjects. |
