Design and implementation of gestational diabetes mellitus intelligent prediction system based on machine learning
To help to effectively identify pregnant women with gestational diabetes mellitus(GDM),intervene in advance for treatment,and reduce potential risks,a GDM intelligent prediction system was designed and developed.Firstly,based on the comparison of 10 machine learning models,data normalization and feature selection were conducted on the clinical dataset based on these models to reduce model computational complexity and unstable features.Secondly,the Stacking algorithm was used to integrate 10 different machine learning models,and two integrated models,Stacking1 and Stacking2,were built to compare their performance.Finally,based on PyQt5,a GDM intelligent prediction system was designed to predict the risk of GDM in pregnant women and provide suggestions.The results show that using 10 machine learning models and two integrated models to predict GDM,it is found that the prediction results of GBDT are higher than other machine learning models.The integrated model Stacking2,combining multiple heterogeneous learners,exhibits high accuracy and reliability,with evaluation indicators of Accuracy,Precision,Recall,and AUC of 0.900 9,0.901 2,0.900 7,and 0.900 7,respectively,all higher than similar models.The intelligent prediction system can effectively predict the risk of GDM,identify susceptible individuals early,and provide popular science knowledge of GDM,thereby strengthen the health management of susceptible individuals and reduce the risk of GDM occurrence.
computer decision support systemmachine learningPyQt5integrated learninggestational diabetes mellitusfeature selection
国家科技期刊平台
NSTL
万方数据
