Summary of Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions Of Diabetic Patient Readmissions, by Zainab Al-zanbouri et al.
Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
by Zainab Al-Zanbouri, Gauri Sharma, Shaina Raza
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores how machine learning (ML) models can accurately predict hospital readmissions for diabetic patients across different demographics, including age, gender, and race. The study compares various models like Deep Learning, Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes. Notably, GBM achieved an F1-score of 84.3% and accuracy of 82.2%, demonstrating its effectiveness in predicting readmissions across demographics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can help predict hospital readmissions for diabetic patients. The research compares different types of models to see which one works best. It finds that a model called Gradient Boosting Machines (GBM) is very good at predicting readmissions, especially when considering factors like age, gender, and race. This study shows how important it is to choose the right machine learning model to make sure predictions are fair and accurate for all patients. |
Keywords
* Artificial intelligence * Boosting * Deep learning * F1 score * Machine learning * Naive bayes
