Interpretable Machine Learning for CVD Prediction

Here is the code used to training the models shown in the paper Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification, available on medRxiv.

We implemented the following models:

• Clinical scores: QRISK3, PCE & SCORE2
• Machine learning models:
  • EBM
  • LightGBM

Citation

If you use this code, please cite our manuscript:

Héctor Climente-González, Min Oh, Urszula Chajewska, Roya Hosseini, Sudipto Mukherjee, Wei Gan, Matthew Traylor, Sile Hu, Ghazaleh Fatemifar, Paul Pangilinan Del Villar, Erik Vernet, Nils Koelling, Liang Du, Robin Abraham, Chuan Li, Joanna M. M. Howson. Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification. medRxiv 2024.01.12.24301213; doi: https://doi.org/10.1101/2024.01.12.24301213