Fetal Health Classification

Project Description

This project aims to analyze and predict fetal health status based on cardiotocograph (CTG) results. Using machine learning techniques, we classify fetal health into three categories: Normal, Suspect, and Pathological. This work was conducted as part of the SY09 course (Data Analysis and Data Mining) at the University of Technology of Compiègne (UTC).

Installation Instructions

To set up this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/fetal-health-classification.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Data Source

The dataset used in this project is from:

• Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318 Link to the original paper

The dataset contains 2126 records of features extracted from Cardiotocogram exams, classified by three expert obstetricians. We performed the following preprocessing steps:

Code Structure

The project is organized as follows:

• Notebooks/: Jupyter notebooks for exploratory data analysis and model development
• Rapports/: Project reports and visualizations
• Utils/: Utils and models

Results and Evaluation

We implemented several machine learning models, including Discriminant Analysis, Random Forest and KNN.

You can find our results on our report and our poster :

• Final Report (Fr)
• Poster (Fr)

Contributors

• Juliette Sacleux
• Samuel Manchajm
• Samuel Beziat

License

This project is released under the GNU GPL 3.0 License. The dataset used in this project is subject to the original authors' terms and conditions.

Contributors

• Juliette Sacleux
• Samuel Manchajm
• Samuel Beziat

For any questions or suggestions, please contact us.