Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles

Sérgio Mosquim Júnior, Juliana de Oliveira

2017

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.

Paper Citation

in Harvard Style

Mosquim Júnior S. and de Oliveira J. (2017). Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles. In - BIOINFORMATICS, (BIOSTEC 2017) ISBN , pages 0-0. DOI: 10.5220/0006170200001488

in Bibtex Style

@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={ - BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006170200001488},
isbn={},
}

in EndNote Style

TY - CONF

JO - - BIOINFORMATICS, (BIOSTEC 2017)
TI - Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles
SN -
AU - Mosquim Júnior S.
AU - de Oliveira J.
PY - 2017
SP - 0
EP - 0
DO - 10.5220/0006170200001488