Summary of Bayesian Networks and Machine Learning For Covid-19 Severity Explanation and Demographic Symptom Classification, by Oluwaseun T. Ajayi et al.
Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification
by Oluwaseun T. Ajayi, Yu Cheng
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A three-stage data-driven approach is presented to uncover hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify causal relationships among symptoms and demographic variables. This output guides an unsupervised machine learning algorithm that clusters patients’ symptoms, followed by training a demographic symptom identification (DSID) model to predict patient symptoms and demographics. The approach is tested on the CDC’s COVID-19 dataset in the US, achieving 99.99% testing accuracy compared to 41.15% for a heuristic ML method. This showcases the viability of the Bayesian network and ML approach in understanding COVID-19 symptom relationships and stratifying patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand COVID-19 is presented. Scientists use three steps to find hidden information about the virus. First, they look at how symptoms are related to demographics using a special method. Then, they group similar symptoms together. Finally, they train a model that can predict what kind of symptoms someone has based on their demographics. The scientists tested this approach on real data from the US Centers for Disease Control and Prevention (CDC) and found it was much better than other methods. |
Keywords
* Artificial intelligence * Bayesian network * Machine learning * Unsupervised
