ArticleInternational journal of environmental research and public health2022
Effective Macrosomia Prediction Using Random Forest Algorithm.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.BMC pregnancy and childbirth · 2025Pooled it
- Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- A machine learning-based risk prediction tool for early identification of invasive pulmonary aspergillosis in immunocompetent patients: a systematic review and meta-analysis.Journal of thoracic disease · 2026Article
- Identifying critical windows for macrosomia prevention: a study on the association between phase-specific dynamic BMI changes and macrosomia risk.Frontiers in endocrinology · 2026Article
- Development of data driven models to accurately estimate density of fatty acid ethyl esters.Scientific reports · 2025Article
- Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.BMC gastroenterology · 2025Article
- Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model.JMIR pediatrics and parenting · 2025Article
- Mapping 10-m Industrial Lands across 1000+ Global Large Cities, 2017-2023.Scientific data · 2025Article
- The novel diagnostic markers for systemic lupus erythematosus and periodontal disease.Frontiers in immunology · 2025Article
- Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.BMC anesthesiology · 2024Article
- Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study.Journal of clinical medicine · 2024Article
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
- Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning.Pharmacogenomics and personalized medicine · 2024Article
- Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2023Article
- Identification and validation of aging-related gene signatures and their immune landscape in diabetic nephropathy.Frontiers in medicine · 2023Article
- A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.BMC pregnancy and childbirth · 2022Observational
- Risk Factors for Macrosomia in Multipara: A Multi-Center Retrospective Study.Children (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.
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