J Med Syst - Diagnosing breast masses in digital mammography using feature selection and ensemble methods.

Tópicos

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{ age(1611) year(1155) adult(843) }
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{ activ(1452) weight(1219) physic(1104) }

Resumo

Methods that can accurately predict breast cancer are greatly needed and good prediction techniques can help to predict breast cancer more accurately. In this study, we used two feature selection methods, forward selection (FS) and backward selection (BS), to remove irrelevant features for improving the results of breast cancer prediction. The results show that feature reduction is useful for improving the predictive accuracy and density is irrelevant feature in the dataset where the data had been identified on full field digital mammograms collected at the Institute of Radiology of the University of Erlangen-Nuremberg between 2003 and 2006. In addition, decision tree (DT), support vector machine-sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic problem in an attempt to predict results with better performance. The results demonstrate that ensemble classifiers are more accurate than a single classifier.

Resumo Limpo

method can accur predict breast cancer great need good predict techniqu can help predict breast cancer accur studi use two featur select method forward select fs backward select bs remov irrelev featur improv result breast cancer predict result show featur reduct use improv predict accuraci densiti irrelev featur dataset data identifi full field digit mammogram collect institut radiolog univers erlangennuremberg addit decis tree dt support vector machinesequenti minim optim svmsmo ensembl appli solv breast cancer diagnost problem attempt predict result better perform result demonstr ensembl classifi accur singl classifi

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