RT info:eu-repo/semantics/article
T1 Breast cancer prediction using fine needle aspiration features and upsampling with supervised machine learning
A1 Shafique, Rahman
A1 Rustam, Furqan
A1 Choi, Gyu Sang
A1 Torre Díez, Isabel de la
A1 Mahmood, Arif
A1 Lipari, Vivian
A1 Rodríguez Velasco, Carmen Lili
A1 Ashraf, Imran
K1 Breast - Cancer - Diagnosis
K1 Cancer research
K1 Breast - Diseases
K1 Mamas -  Cáncer
K1 Cytology
K1 Mamas - Citología
K1 Principal components analysis
K1 Análisis multivariante
K1 Singular value decomposition
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 Oncology
K1 3207.13 Oncología
K1 1203.04 Inteligencia Artificial
AB Simple Summary: 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.
PB MDPI
SN 2072-6694
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/63340
UL https://uvadoc.uva.es/handle/10324/63340
LA eng
NO Cancers, 2023, Vol. 15, Nº. 3, 681
NO Producción Científica
DS UVaDOC
RD 03-jun-2024