https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/19893.2
type: CreativeWork
name: Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques. Development of 3rd Trimester Machine Learning Prediction Models and Identification of Important Features Using Dimensionality Reduction Techniques
description: Background: Large-for-gestational-age (LGA) is a common pregnancy complication, associated with high maternal BMI and diabetes. Despite its gravity, standard prediction methods, such as ultrasounds are inaccurate. Objective: application of machine learning methods to develop LGA prediction models for ethnically diverse datasets and provide a benchmark for future LGA prediction work. Methods: Two retrospective datasets were used: Born In Bradford (BiB) and NHS, each including a large percentage of women of South Asian ethnicity. After appropriate data preparation, LGA classification models were developed, and imbalanced learning strategies were applied. Additionally, using data reduction, important features within the datasets were reported. Results: Baseline BiB models achieved 9% sensitivity, 56% precision, and 26% F0.5, BiB-GDM (containing only GDM women) models achieved 41% sensitivity, 60% precision, and 55% F0.5. Applying random undersampling increased sensitivity scores to 72% for BiB models and 80% for BiB-GDM. Cost sensitive learning methods achieved 36% F0.5 score for BiB models and 57% for BiB-GDM models. Threshold tuning improved the F0.5 scores models to 47% and 66 % in BiB and BiB-GDM, respectively. Using data reduction, important features were the minimum and maximum Abdominal Circumference (AC) and Estimated Foetal Weight (EFW) ultrasound measurements in BiB-GDM dataset. While they were the mean and proportion of high Blood Glucose (BG) measurements in the NHS dataset. Conclusions: machine learning algorithms are not superior to Logistic Regression in LGA predictive performance. Threshold tuning was an appropriate method for handling data imbalance and maximising F0.5 scores. Finally, ultrasound measurements and BG self-monitoring data were important features in LGA prediction.
abstract: Background: Large-for-gestational-age (LGA) is a common pregnancy complication, associated with high maternal BMI and diabetes. Despite its gravity, standard prediction methods, such as ultrasounds are inaccurate. Objective: application of machine learning methods to develop LGA prediction models for ethnically diverse datasets and provide a benchmark for future LGA prediction work. Methods: Two retrospective datasets were used: Born In Bradford (BiB) and NHS, each including a large percentage of women of South Asian ethnicity. After appropriate data preparation, LGA classification models were developed, and imbalanced learning strategies were applied. Additionally, using data reduction, important features within the datasets were reported. Results: Baseline BiB models achieved 9% sensitivity, 56% precision, and 26% F0.5, BiB-GDM (containing only GDM women) models achieved 41% sensitivity, 60% precision, and 55% F0.5. Applying random undersampling increased sensitivity scores to 72% for BiB models and 80% for BiB-GDM. Cost sensitive learning methods achieved 36% F0.5 score for BiB models and 57% for BiB-GDM models. Threshold tuning improved the F0.5 scores models to 47% and 66 % in BiB and BiB-GDM, respectively. Using data reduction, important features were the minimum and maximum Abdominal Circumference (AC) and Estimated Foetal Weight (EFW) ultrasound measurements in BiB-GDM dataset. While they were the mean and proportion of high Blood Glucose (BG) measurements in the NHS dataset. Conclusions: machine learning algorithms are not superior to Logistic Regression in LGA predictive performance. Threshold tuning was an appropriate method for handling data imbalance and maximising F0.5 scores. Finally, ultrasound measurements and BG self-monitoring data were important features in LGA prediction.
author: Sabouni, Sumaia
language: en
keywords: Large for gestational age, Gestational diabetes, Macrosomia, Obesity, Machine learning, Prediction, Class imbalance, Algorithms, Ethnically diverse datasets
date published: 2023
date modified: 2026-07-24T01:13:22Z
provider: University of Bradford
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Bradford