Data Science Project: Predicting Clinical Outcomes Using Machine Learning

This project aims to predict clinical outcomes in patients using clinical predictors and biomarkers. We use various machine learning techniques to analyze protein panels and their diagnostic/prognostic effects on two heart diseases.

Project Structure

The project is structured as follows:

• 3_PAD_modeling.ipynb and 4_PAD_modelin.ipynb: These Jupyter notebooks contain the main modeling work for the project.
• EDA_2.ipynb and EDA_PAD.ipynb: These notebooks contain exploratory data analysis (EDA) of the protein panels.
• scripts/predictiveModeling.py: This Python script contains the main predictive modeling functions used in the notebooks.
• requirements.txt: This file lists the Python dependencies required for this project.
• reports/: This directory contains various reports and findings from the analysis.
• jupyter_config.py: This Python script contains Jupyter notebook configuration settings.

Setup

To set up the project, first clone the repository. Then, install the required Python dependencies using pip:

pip install -r requirements.txt

Usage

To run the predictive models, open the Jupyter notebooks (3_PAD_modeling.ipynb and 4_PAD_modelin.ipynb) and run the cells in order.

Contributing

Contributions are welcome. Please open an issue to discuss your idea or submit a pull request.

License

This project is licensed under the terms of the MIT license.