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01-09-2016 | Systems-Level Quality Improvement

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

Authors: Joana Diz, Goreti Marreiros, Alberto Freitas

Published in: Journal of Medical Systems | Issue 9/2016

Abstract

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.

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Title: Applying Data Mining Techniques to Improve Breast Cancer Diagnosis
Authors: Joana Diz
Goreti Marreiros
Alberto Freitas
Publication date: 01-09-2016
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 9/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-016-0561-y

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Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

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