TY  - JOUR
AV  - public
JF  - Cancers
A1  - Shafique, Rahman
A1  - Rustam, Furqan
A1  - Choi, Gyu Sang
A1  - Díez, Isabel de la Torre
A1  - Mahmood, Arif
A1  - Lipari, Vivian
A1  - Rodríguez Velasco, Carmen Lilí
A1  - Ashraf, Imran
IS  - 3
N2  - 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
ID  - uninimx5662
TI  - Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning
Y1  - 2023///
VL  - 15
UR  - http://doi.org/10.3390/cancers15030681
SN  - 2072-6694
KW  - breast cancer prediction; feature selection; fine-needle aspiration features; principal component analysis; singular value decomposition; deep learning
ER  -