eprintid: 5662 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/56/62 datestamp: 2023-02-01 23:30:09 lastmod: 2023-02-01 23:30:10 status_changed: 2023-02-01 23:30:09 type: article metadata_visibility: show creators_name: Shafique, Rahman creators_name: Rustam, Furqan creators_name: Choi, Gyu Sang creators_name: Díez, Isabel de la Torre creators_name: Mahmood, Arif creators_name: Lipari, Vivian creators_name: Rodríguez Velasco, Carmen Lilí creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: vivian.lipari@uneatlantico.es creators_id: carmen.rodriguez@uneatlantico.es creators_id: title: Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: breast cancer prediction; feature selection; fine-needle aspiration features; principal component analysis; singular value decomposition; deep learning abstract: 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 publication: Cancers volume: 15 number: 3 pagerange: 681 id_number: doi:10.3390/cancers15030681 refereed: TRUE issn: 2072-6694 official_url: http://doi.org/10.3390/cancers15030681 access: open language: en citation: 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 document_url: http://repositorio.unic.co.ao/id/eprint/5662/1/cancers-15-00681.pdf