Uriel Abe Contardi 
, Paulo Rogério Scalassara & Douglas Vieira Thomaz
Abstract: Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. Owing to the relevance of obtaining better detection tools, this work describes the development and optimization of support vector machines for the classification of the types of such cancer. Tests were performed using the breast cancer dataset of the University of Wisconsin Hospitals, USA, available at the Machine Learning Repository of the University of California Irvine. The radial basis function kernel was selected for the classifier and its hyperparameters were refined using two methods: particle swarm optimization and genetic algorithms. The results for the first method exhibited 97.71% accuracy, 96.30% sensitivity, and 98.65% of selectivity. On the other hand, using the second method, the accuracy was 95.78%, with sensitivity and selectivity of 96.73% and 95.25%, respectively. Therefore, there is an indication that these search algorithms are viable tools to optimize machine learning models for the purpose of breast cancer classification.
Keywords: evolutionary algorithm, swarm intelligence algorithm, neural models, optimization, neoplastic tumor.
DOI code: 10.21528/lnlm-vol20-no2-art2
PDF file: vol20-no2-art2.pdf
BibTex file: vol20-no2-art2.bib