chml-public
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (6.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: nso-informatics
- License: mit
- Language: Python
- Default Branch: main
- Size: 61.5 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
CHML
This project aims to enhance the screening process for Congenital Hypothyroidism (CH) using advanced machine learning techniques. By leveraging a large dataset from Newborn Screening Ontario (NSO), the project evaluates various classifiers and resampling methods to improve the Positive Predictive Value (PPV) while maintaining 100% sensitivity. The ultimate goal is to reduce false positives, thereby minimizing unnecessary retests and alleviating stress for families.
The runtime component of this project is likely to be useful for other projects that require the use of machine learning models for classification tasks. The project is designed to be modular, allowing for easy integration of new classifiers, resampling methods, and datasets. The project is also designed to be user-friendly, with a small variety of example scripts that demonstrate how to use the runtime component.
Requirements
- Python 3.9
- Pipenv
- Jupyter Notebook (optional)
Owner
- Name: Newborn Screening Ontario
- Login: nso-informatics
- Kind: organization
- Email: NewbornScreening@cheo.on.ca
- Website: https://www.newbornscreening.on.ca
- Repositories: 1
- Profile: https://github.com/nso-informatics
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: CHML-Public
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Alexander
family-names: De Furia
email: adefu020@uottawa.ca
affiliation: >-
University of Ottawa, School of Electrical Engineering
and Computer Science
orcid: 'https://orcid.org/0009-0000-9101-3947'
- given-names: Paula
family-names: Branco
email: pbranco@uottawa.ca
affiliation: 'University of Ottawa, School of Electrical Engineering'
orcid: 'https://orcid.org/0000-0002-9917-3694'
- given-names: 'Matthew '
family-names: Henderson
email: mhenderson@cheo.on.ca
affiliation: Newborn Screening Ontario
repository-code: 'https://github.com/nso-informatics/CHML-Public'
abstract: >-
Source code repository for "Comprehensive Comparison of
Machine Learning and Resampling Algorithms for Thyroid
Stimulating Hormone Based Congenital Hypothyroidism
Screening" (De Furia, et al. 2024). This repository is
used to compare and evaluate machine learning and
resampling algorithms, as well as implement a custom
variation of Gaussian noise resampling. This repository
focuses on the managing of results and can likely be
useful in other, non CHML projects.
license: MIT
commit: 21edead9faabbd1001bd010b9cda1b59ab286d0b
version: 1.0.0
date-released: '2024-10-10'
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Dependencies
- datascroller ==1.4.1
- dtale ==3.9.0
- h5py ==3.10.0
- imbalanced-learn ==0.11.0
- ipykernel ==6.29.0
- matplotlib ==3.8.2
- openpyxl ==3.1.2
- pandas ==2.1.4
- phik ==0.12.4
- psycopg2-binary *
- requests ==2.31.0
- scikit-learn ==1.3.2
- scikit-optimize ==0.9.0
- seaborn ==0.13.2
- sweetviz ==2.3.1
- xgboost ==1.7.1