https://github.com/matteofasulo/bayesianheartdisease
Heart Disease Risk Assessment using Bayesian Networks
https://github.com/matteofasulo/bayesianheartdisease
bayesian-network bayesian-networks pgmpy python3
Last synced: 7 months ago
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Heart Disease Risk Assessment using Bayesian Networks
- Host: GitHub
- URL: https://github.com/matteofasulo/bayesianheartdisease
- Owner: MatteoFasulo
- License: mit
- Created: 2023-12-20T10:46:09.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-09T16:22:03.000Z (about 2 years ago)
- Last Synced: 2025-06-05T06:26:01.808Z (9 months ago)
- Topics: bayesian-network, bayesian-networks, pgmpy, python3
- Language: Jupyter Notebook
- Homepage: https://matteofasulo.github.io/BayesianHeartDisease/report.pdf
- Size: 17.6 MB
- Stars: 5
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Bayesian Network for Heart Disease Risk Assessment
This repository encompasses the code and report for the "Fundamentals of Artificial Intelligence and Knowledge Representation (Mod. 3)" course at Alma Mater Studiorum Università di Bologna.
## Authors
- [Matteo Fasulo](https://github.com/MatteoFasulo)
- [Luca Tedeschini](https://github.com/LucaTedeschini)
- [Antonio Gravina](https://github.com/GravAnt)
- [Luca Babboni](https://github.com/ElektroDuck)
## Report
The pdf report is available [here](https://matteofasulo.github.io/BayesianHeartDisease/report.pdf).
## Abstract
Cardiovascular disease (CVD) remains a significant cause of mortality in Europe, imposing both health and economic challenges. Timely and accurate prediction is crucial for effective prevention and intervention strategies. Identifying modifiable and non-modifiable risk factors is essential, as lifestyle changes can significantly impact individual health.
Bayesian networks (BNs) have emerged as valuable tools in healthcare for handling complex data and analyzing interactions among various risk factors. They've proven successful in assessing CVD risk, aiding real-time diagnosis, and predicting hidden patient conditions.
Our work, inspired by [Ordovas et al. (2023)](https://doi.org/10.1016/j.cmpb.2023.107405), aimed to replicate their BN-based CVD risk prediction using a different dataset. Additionally, we sought to explore the broader potential of BNs in CVD risk assessment, conducting in-depth analyses beyond the original paper.
## How to clone the repository?
Since the repository contains a submodule, the following command should be used to clone the repository:
```bash
git clone --recursive https://github.com/MatteoFasulo/BayesianHeartDisease.git
```
## Dashboard
> The Web App is publicly available at [heart-disease-risk.streamlit.app](https://heart-disease-risk.streamlit.app)
## Source
Datasets used are accessible in the UCI Machine Learning Repository's Index of heart disease datasets: [UCI Heart Disease Datasets](https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/)
## Dataset
>fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved March 2024 from [Kaggle](https://www.kaggle.com/fedesoriano/heart-failure-prediction).
## Acknowledgements
1. Hungarian Institute of Cardiology, Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.
## References
[1] Wilkins, E., et al. (2017). European Cardiovascular Disease Statistics 2017. European Heart Network. [CVD Statistics Report](http://www.ehnheart.org/images/CVD-statistics-report-August-2017.pdf)
[2] Mahmood, S. S., et al. (2014). The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet (London, England), 383(9921), 999–1008. [DOI](https://doi.org/10.1016/S0140-6736(13)61752-3)
[3] WHO CVD Risk Chart Working Group (2019). World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. The Lancet. Global health, 7(10), e1332–e1345. [DOI](https://doi.org/10.1016/S2214-109X(19)30318-3)
[4] Jensen, Finn & Nielsen, Thomas. (2007). Bayesian Network and Decision Graphs. [DOI](https://doi.org/10.1007/978-0-387-68282-2).
## Code of Conduct
Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By participating in this project, you agree to abide by its terms.
## License
This project is licensed under the [MIT License](LICENSE).