ArticleComputational intelligence and neuroscience2022
Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- A Lifestyle-Based Fuzzy-Enhanced ANN Model for Early Prediction of Type 2 Diabetes and Personalized Management in the North Indian Population.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.Scientific reports · 2025Article
- Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.BMC medical informatics and decision making · 2025Article
- Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.American journal of cancer research · 2025Article
- The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.Clinical diabetes and endocrinology · 2024Review
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gestational diabetes mellitus (GDM) is one of the risk factors for fetal dysplasia and maternal pregnancy difficulties. Therefore, the prediction of the risk of GDM in advance has become a big demand for millions of families. Therefore, machine learning technology is adopted to study GDM prediction. Firstly, the data is preprocessed, and the mean value is used for outlier processing. After preprocessing of the data, the IV value method is used to screen the features. Of the 83 features in the original sample data, 40 important features are screened out through feature engineering. On this basis, Logistics regression model, Lasso-Logistics, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (Xgboost), Light Gradient Boosting Machine (Lightgbm), and Gradient Boosting Categorical Features (Catboost) are established, and multiple learners are integrated. Finally, the constructed model is tested on data sets. The accuracy of the proposed model is 80.3%, the accuracy is 74.6%, the recall rate is 79.3%, and the running time is only 2.53 seconds. This means that the proposed model is superior to the previous models in terms of accuracy, precision, recall rate, and F1 value, and the time consumption is also in line with the actual engineering requirements. The proposed scheme provides some ideas for the research of machine learning technology in disease prediction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.