@article{unic5662,
          author = {Rahman Shafique and Furqan Rustam and Gyu Sang Choi and Isabel de la Torre D{\'i}ez and Arif Mahmood and Vivian Lipari and Carmen Lil{\'i} Rodr{\'i}guez Velasco and Imran Ashraf},
          number = {3},
           pages = {681},
          volume = {15},
           title = {Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning},
         journal = {Cancers},
            year = {2023},
             url = {http://repositorio.unic.co.ao/id/eprint/5662/},
        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},
        keywords = {breast cancer prediction; feature selection; fine-needle aspiration features; principal component analysis; singular value decomposition; deep learning}
}