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Authors: Sérgio Mosquim Júnior 1 and Juliana de Oliveira 2

Affiliations: 1 São Paulo State University and Uppsala University, Brazil ; 2 São Paulo State University, Brazil

ISBN: 978-989-758-214-1

ISSN: 2184-4305

Keyword(s): Data Mining, Breast Cancer, Decision Trees, Artificial Neural Networks.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning

Abstract: Breast cancer has the second highest incidence among all cancer types and is the fifth cause of cancer related death among women. In Brazil, breast cancer mortality rates have been rising. Cancer classification is intricate, mainly when differentiating subtypes. In this context, data mining becomes a fundamental tool to analyze genotypic data, improving diagnostics, treatment and patient care. As the data dimensionality is problematic, methods to reduce it must be applied. Hence, the present study aims at the analysis of two data mining methods (i.e., decision trees and artificial neural networks). Weka® and MATLAB® were used to implement these two methodologies. Decision trees appointed important genes for the classification. Optimal artificial neural network architecture consists of two layers, one with 99 neurons and the other with 5. Both data mining techniques were able to classify data with high accuracy.

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Paper citation in several formats:
Mosquim Júnior, S. and de Oliveira, J. (2017). Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1 ISSN 2184-4305, pages 168-175. DOI: 10.5220/0006170201680175

@conference{bioinformatics17,
author={Sérgio {Mosquim Júnior}. and Juliana {de Oliveira}.},
title={Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={168-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006170201680175},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles
SN - 978-989-758-214-1
IS - 2184-4305
AU - Mosquim Júnior, S.
AU - de Oliveira, J.
PY - 2017
SP - 168
EP - 175
DO - 10.5220/0006170201680175

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