Design And Implementation Of Diabetes Risk Prediction System For Community Residents

In recent years,China's economy has developed rapidly,and the national lifestyle has changed a lot compared to the previous ones.The number of people with diabetes worldwide has increased particularly rapidly,and many people do not know after the illness,especially for the health risks of patients.Large,at the same time high treatment costs dragged down many families,seriously reducing the family's happiness index.At present,the diagnosis of diabetes in most medical institutions depends on the doctor's personal experience and physical examination data.Therefore,there are certain drawbacks in the diagnosis of diseases.It requires doctors to have superb medical skills and sufficient experience,otherwise it may be misdiagnosed or missed.If you can't heal at the best time,it will probably aggravate the deterioration of the disease.This phenomenon is something we don't want to see.At present,smart medical care has become the trend of the times.If we combine diabetes and machine learning,and use machine learning algorithms to assist doctors in diagnosis,it will greatly improve the scientific nature of diagnosis and effectively overcome the subjective problems of doctors' empirical diagnosis..In view of the above,this paper builds a diabetes risk prediction model and designs and implements a diabetes risk prediction system based on the laboratory project.The specific contents include(1)algorithm selection: this paper studies the characteristics of diabetes by consulting the literature,studies many existing disease risk prediction models,and then combines the characteristics of the original data samples,selects random forest,XGBoost and CatBoost three algorithms.Modeling.(2)Data preprocessing: In order to ensure that the best results can be achieved in the modeling in order to ensure the best results in the modeling,this paper removes various problems and adjusts the format of the data.,processing the data into a form of data suitable for modeling.(3)Feature selection: The data dimension after preprocessing is still very large,so many fields are not conducive to modeling,andthere are many fields that have little effect on modeling,so this paper selects features and uses IV value analysis to select features.Finally,11 features were selected as the final model input variables.(4)Modeling and experimental analysis: using the features selected in the method described in(3),using the three algorithms described in(1)to model separately,and to tune the final model,each model has Based on the optimal prediction results,the model was compared and evaluated using the accuracy,recall,F1 and running time indicators,and the CatBoost algorithm model was selected to be embedded in the diabetes risk prediction system.(5)System design and implementation: Fully analyze the demand for diabetes risk prediction system,complete system design and implementation,and perform functional test on the system.