کارآمد ترین الگوریتم های ماشین لرنینگ در پیش بینی دیابت بارداری: مروری سیستماتیک
کد: G-1790
نویسندگان: Atefeh Pagheh ℗, Amir Hossein Daeechini *, Hossein Valizadeh Laktrashi , Zahra Daeechini
زمان بندی: زمان بندی نشده!
برچسب: پردازش سیگنال های پزشکی
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Introduction: Gestational diabetes mellitus (GDM) is a condition in which the body is unable to effectively utilize insulin, leading to insulin resistance and glucose intolerance. Timely prediction of this disorder can aid in better management of the health of both mother and baby. The purpose of this study is to systematically review published papers on gestational diabetes prediction using machine learning (ML) algorithms and to introduce and compare the most efficient of these algorithms. Methods: A total of 1062 publications were found, of which 34 studies were considered in this review. The authors conducted a systematic search in databases including PubMed, Scopus, and Embase using the keywords "Artificial Intelligence," "Predictive Modeling," "Machine Learning," and "gestational diabetes" from 2020 to March 2025. The screening, extraction and analysis of the identified articles were performed by following the PRISMA guidelines. Results: A total of thirty-four articles met the inclusion criteria. Parameters for selecting the most efficient machine learning algorithm include accuracy, sensitivity, specificity, and precision criteria. Among them, the Xgboost algorithm was identified as the best and most efficient machine learning algorithm for predicting gestational diabetes in 30% of the articles (n = 10). Other algorithms recognized as the best and most effective methods in these papers included Random Forest (n = 8), K-Nearest Neighbors (n = 4), Logistic Regression (n = 3), Artificial Neural Networks (n = 3), Gradient Boosting Machine (n = 4), and Bayesian Classifier (n = 2). Analysis of variables also showed that age, body mass index, and family history of diabetes are the main influential factors in predicting this disease. Conclusion: Machine learning algorithms are recognized as effective tools in predicting GDM. The findings of this study can serve as a basis for future research aimed at improving GDM prediction and management, contributing to optimal decision-making in pregnancy health.
کلمات کلیدی
Gestational Diabetes, Artificial Intelligence, Machine Learning
