Sensitivity Analysis of Stroke Predictors Using Structural Equation Modeling and Bayesian Networks

This repository contains the code used on the paper of same name published on the IEEE CIBCB'22 conference:

H. C. R. Oliveira, S. Yanushkevich and M. Almekhlafi, "Sensitivity Analysis of Stroke Predictors Using Structural Equation Modeling and Bayesian Networks," 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2022, pp. 1-8, doi: 10.1109/CIBCB55180.2022.9863028.

Reproducibility

To make the experiments presented here reproducible, many seed parameters were fixed into the code. More over, the BNs created in this paper were exported using the PyAgrum library and can be found in this repository in files with .bif extension.

Data

The data used for this study is freely available on Kaggle: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset

Contact information

Email: heldercro@gmail.com

Web: http://helderc.xyz