Publication: Comparative Study on Data Mining Techniques Applied to Breast Cancer Gene Expression Profiles
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Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
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Volume Title
Publisher
Scitepress
Type
Work presented at event
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Acesso aberto
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 (R) and MATLAB (R) 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.
Description
Keywords
Data Mining, Breast Cancer, Decision Trees, Artificial Neural Networks
Language
English
Citation
Proceedings Of The 10th International Joint Conference On Biomedical Engineering Systems And Technologies, Vol 3: Bioinformatics. Setubal: Scitepress, p. 168-175, 2017.
