relation: http://repositorio.unini.edu.mx/id/eprint/5662/ canonical: http://repositorio.unini.edu.mx/id/eprint/5662/ title: Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning creator: Shafique, Rahman creator: Rustam, Furqan creator: Choi, Gyu Sang creator: Díez, Isabel de la Torre creator: Mahmood, Arif creator: Lipari, Vivian creator: Rodríguez Velasco, Carmen Lilí creator: Ashraf, Imran subject: Ingeniería description: Breast cancer is prevalent in women and the second leading cause of death. Conventional breast cancer detection methods require several laboratory tests and medical experts. Automated breast cancer detection is thus very important for timely treatment. This study explores the influence of various feature selection technique to increase the performance of machine learning methods for breast cancer detection. Experimental results shows that use of appropriate features tend to show highly accurate prediction date: 2023 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_4 identifier: http://repositorio.unini.edu.mx/id/eprint/5662/1/cancers-15-00681.pdf identifier: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Breast cancer is prevalent in women and the second leading cause of death. Conventional breast cancer detection methods require several laboratory tests and medical experts. Automated breast cancer detection is thus very important for timely treatment. This study explores the influence of various feature selection technique to increase the performance of machine learning methods for breast cancer detection. Experimental results shows that use of appropriate features tend to show highly accurate prediction metadata Shafique, Rahman; Rustam, Furqan; Choi, Gyu Sang; Díez, Isabel de la Torre; Mahmood, Arif; Lipari, Vivian; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2023) Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning. Cancers, 15 (3). p. 681. ISSN 2072-6694 relation: http://doi.org/10.3390/cancers15030681 relation: doi:10.3390/cancers15030681 language: en