Comparison of bioinspired algorithms applied to cancer database
Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chance...
- Autores:
-
Silva, Jesús
Villareal-González, Reynaldo
Varela, Noel
Maco, José
Villón, Martín
Marín–González, Freddy
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7700
- Acceso en línea:
- https://hdl.handle.net/11323/7700
https://doi.org/10.1007/978-981-15-7234-0_87
https://repositorio.cuc.edu.co/
- Palabra clave:
- Big data
Machine learning
Cancer prediction
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| Summary: | Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database. |
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