Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

OBJECTIVE: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. METHODS: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Mac...

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Detalles Bibliográficos
Autores principales: Ozcan, Irem, Aydin, Hakan, Cetinkaya, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: West Asia Organization for Cancer Prevention 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924317/
https://www.ncbi.nlm.nih.gov/pubmed/36308351
http://dx.doi.org/10.31557/APJCP.2022.23.10.3287

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924317/
https://www.ncbi.nlm.nih.gov/pubmed/36308351
http://dx.doi.org/10.31557/APJCP.2022.23.10.3287

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