Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

• Tipo: Artigo em Revista Científica Internacional
• Data: 2016

Autores

Diz, J (Autor) Outra; Marreiros, G (Autor) Outra; Freitas A (Autor) FMUP

Revista

Título: Journal of Medical Systems

Vol. 40

ISSN: 0148-5598

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 39 Citações

Scopus - 56 Citações

Outras Informações

ID Authenticus: P-00K-R2Q

DOI: 10.1007/s10916-016-0561-y

Abstract (EN): In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7