Copyright
©The Author(s) 2021.
World J Gastroenterol. Oct 14, 2021; 27(38): 6399-6414
Published online Oct 14, 2021. doi: 10.3748/wjg.v27.i38.6399
Published online Oct 14, 2021. doi: 10.3748/wjg.v27.i38.6399
Characteristics | Support vector machine | Random forest | Decision trees | Deep neural networks | Context | Ref. |
High dimensional data | High | High | Moderate | High | Performance | Shen et al[12]; Goodfellow et al[26] |
Overlapped classes | Low | Low | Low | High | ||
Imbalance datasets | Moderate | High | Low | Moderate | ||
Non-linear data | Moderate | High | Moderate | High | ||
Larger dataset | Moderate1 | High1 | Low | High | ||
Outliers | Moderate | Moderate | Low | High | Robustness | Shen et al[12]; Yu et al[20] |
Over-fitting | Moderate | High | Low | High | ||
Handling of missing values | Poor | Good | Good | Good | ||
Reproducibility | High | High | High | Moderate | Complexity | Yu et al[20] |
Interpretability | Moderate | Moderate | High | Low |
- Citation: Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World J Gastroenterol 2021; 27(38): 6399-6414
- URL: https://www.wjgnet.com/1007-9327/full/v27/i38/6399.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i38.6399