DocumentCode :
3681400
Title :
Pre-processing flow for enhancing learning from medical data
Author :
Sebastian Muresan;Ioana Faloba;Camelia Lemnaru;Rodica Potolea
Author_Institution :
Computer Science Department, Technical University of Cluj-Napoca, Romania
fYear :
2015
Firstpage :
27
Lastpage :
34
Abstract :
Data enhancement is an essential operation when dealing with incomplete and imbalanced data sets. Further classification on such data might prove to be a difficult task. This paper tackles such issues in a specific learning context - medical treatment prediction for breast cancer. We process the problem specific medical data starting from the preparation phase. We apply several data cleaning and selection steps. The resulting data proved to possess an insufficient quality for the learning process. Therefore, we propose and apply several data enhancement steps, such as imputation for handling missing values, feature selection for reducing the dimensionality of the attribute space and a modified version of the SMOTE oversampling algorithm to tackle data imbalance in conjunction with incompleteness. Evaluations of the entire pre-processing flow, performed on the available medical data, have indicated significant improvements in classification performance.
Keywords :
"Feature extraction","Data mining","Surgery","Chemotherapy","Uncertainty","Arrays"
Publisher :
ieee
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/ICCP.2015.7312601
Filename :
7312601
Link To Document :
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