Summary of An Explainable Ai Model For Predicting the Recurrence Of Differentiated Thyroid Cancer, by Mohammad Al-sayed Ahmad et al.
An Explainable AI Model for Predicting the Recurrence of Differentiated Thyroid Cancer
by Mohammad Al-Sayed Ahmad, Jude Haddad
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
GrooveSquid.com Paper Summaries
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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 presents a machine learning approach to predict the recurrence of differentiated thyroid cancer (DTC), particularly papillary and follicular varieties, using deep learning models. By analyzing clinicopathological features of patients, the model achieves high accuracy rates during training (98%) and testing (96%). To improve interpretability, techniques like LIME and Morris Sensitivity Analysis are employed, providing valuable insights into decision-making processes. The results demonstrate the potential benefits of combining deep learning with interpretability techniques in identifying thyroid cancer recurrences, informing therapeutic choices, and personalizing treatment approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses machine learning to help predict when thyroid cancer might come back. They use special computer models that look at patient information like how big the tumor is or if it’s moved to other parts of the body. The model is really good at guessing when cancer will return, and by using special tools to understand how it makes decisions, doctors can make better choices about treatment for each patient. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
