Summary of Comparison Of Machine Learning Classification Algorithms and Application to the Framingham Heart Study, by Nabil Kahouadji
Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Study
by Nabil Kahouadji
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 algorithms in healthcare can exacerbate social injustices and health inequities by perpetuating biases during development and deployment. The researchers focus on generalizability impediments that occur during algorithm development and post-deployment, using the Framingham coronary heart disease data as a case study. They investigate eight classification algorithms under four training/testing scenarios to test their generalizability and potential to propagate biases. Results show that Extreme Gradient Boosting and Support Vector Machine are flawed when trained on unbalanced datasets. The double discriminant scoring of type I is found to be the most generalizable, consistently outperforming other algorithms across scenarios. Additionally, the paper introduces a methodology for extracting an optimal variable hierarchy for classification algorithms, illustrated on Framingham coronary heart disease data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning in healthcare can make things worse if it’s not done carefully. The researchers looked at how machine learning models can be biased and perpetuate social injustices. They used real-world data to test different types of machine learning models and found that some models are better than others when it comes to being fair. One model, called double discriminant scoring of type I, was particularly good at making accurate predictions without favoring one group over another. The researchers also developed a new way to improve the performance of machine learning models by choosing the right variables to use. |
Keywords
* Artificial intelligence * Classification * Extreme gradient boosting * Machine learning * Support vector machine
