Summary of Coxkan: Kolmogorov-arnold Networks For Interpretable, High-performance Survival Analysis, by William Knottenbelt et al.
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis
by William Knottenbelt, Zeyu Gao, Rebecca Wray, Woody Zhidong Zhang, Jiashuai Liu, Mireia Crispin-Ortuzar
First submitted to arxiv on: 6 Sep 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 The proposed CoxKAN model offers a balance between performance and interpretability in survival analysis. This is particularly crucial in fields like medicine where practitioners need to make informed decisions about patient care. The study builds upon previous work with Kolmogorov-Arnold Networks (KANs) by introducing CoxKAN, a Cox proportional hazards KAN for interpretable and high-performance survival analysis. The model is evaluated on synthetic and real medical datasets, demonstrating its ability to accurately recover symbolic formulae for the hazard function, perform automatic feature selection, and outperform traditional methods like the Cox proportional hazards model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoxKAN is a new way of doing survival analysis that’s both accurate and easy to understand. It can help doctors make better decisions about patient care by showing how different factors affect a person’s risk of getting sick or dying. The researchers tested CoxKAN on many datasets, including some with real medical data, and found that it worked well and was often better than other methods. This is important because it helps us understand the complex relationships between different factors that can affect our health. |
Keywords
» Artificial intelligence » Feature selection
