Summary of Risk Factor Identification in Osteoporosis Using Unsupervised Machine Learning Techniques, by Mikayla Calitis
Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
by Mikayla Calitis
First submitted to arxiv on: 24 May 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 A novel clustering-based method is proposed to investigate the reliability of identified risk factors associated with osteoporosis using electronic medical records. The CLustering Iterations Framework (CLIF) adapts three components: clustering, feature selection, and principal feature identification. Wasserstein distance is used to identify principal features, while ANOVA and ablation tests select influential features from a dataset. The study finds that some risk factors supported by existing works are endorsed by significant clusters, but the reliability of others is weakened. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses computer algorithms to look at medical records and figure out which risk factors might actually be connected to osteoporosis. They create a special tool called CLIF that can adjust different parts of the process to make it work better. The researchers use this tool to find important features in the data and see if some of the existing ideas about what causes osteoporosis are correct or not. |
Keywords
* Artificial intelligence * Clustering * Feature selection
