Summary of Disparate Model Performance and Stability in Machine Learning Clinical Support For Diabetes and Heart Diseases, by Ioannis Bilionis et al.
Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases
by Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque, Carlos Castillo
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates the predictive performance of Machine Learning (ML) algorithms in biomedical informatics, focusing on chronic disease datasets. The study reveals widespread sex- and age-related inequities in these datasets and their derived ML models. A novel analytical framework is introduced to address these disparities, combining systematic arbitrariness with traditional metrics like accuracy and data complexity. The analysis of over 25,000 individuals with chronic diseases shows mild sex-related disparities favoring males, significant age-related differences favoring younger patients, and inconsistent predictive accuracy for older patients linked to higher data complexity and lower model performance. These findings highlight the importance of addressing representativeness in training data and model arbitrariness before deploying models in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computer programs can predict diseases based on medical data. The study found that these programs often do better for men and younger people than women and older people. This is because the programs are trained using medical records of mostly men and young people, so they’re not very good at predicting diseases in women and older people. To fix this problem, the researchers developed a new way to analyze the data and found that even when the programs do better for men and younger people, they can still be useful for women and older people if we use more complex medical records. |
Keywords
» Artificial intelligence » Machine learning
