phdfinalcode
Complete code of PhD: "Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques"
Science Score: 31.0%
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Complete code of PhD: "Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques"
Basic Info
- Host: GitHub
- Owner: ssabouni
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 8.72 MB
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 3 years ago
· Last pushed almost 3 years ago
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README.md
phdFinalCode
Complete code of PhD: "Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques"
Owner
- Name: Sumaia Sabouni
- Login: ssabouni
- Kind: user
- Location: United Kingdom
- Company: The University of Bradford
- Repositories: 2
- Profile: https://github.com/ssabouni
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Prediction of Large for Gestational Age Infants in
Ethnically Diverse Datasets Using Machine Learning
Techniques
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Sumaia
family-names: Sabouni
email: sumaia.sabouni@gmail.com
affiliation: University of Bradford
repository-code: 'https://github.com/ssabouni/phdFinalCode'
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, providing a reliability index to enhance
standard clinical methods.
Methods: Two retrospective datasets were used: Born In
Bradford (BiB) and NHS, each including a significant
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.
license: MIT
date-released: 2023