J Med Syst - An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method.

Tópicos

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{ model(2341) predict(2261) use(1141) }
{ perform(999) metric(946) measur(919) }
{ cancer(2502) breast(956) screen(824) }
{ result(1111) use(1088) new(759) }
{ data(1737) use(1416) pattern(1282) }
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{ blood(1257) pressur(1144) flow(957) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ gene(2352) biolog(1181) express(1162) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.

Resumo Limpo

paper develop novel featur select algorithm base genet algorithm ga use specif devis tracebas separ criterion accord score class separ variabl separ criterion measur signific featur subset independ specif classif addit mutual inform matrix variabl use featur classif prior knowledg cardin featur subset requir experi perform use standard lung cancer dataset obtain solut verifi three differ classifi includ support vector machin svm backpropag neural network bpnn knearest neighbor knn compar obtain whole featur set fscore correlationbas featur select method comparison result show propos intellig system good diagnosi perform can use promis tool lung cancer diagnosi

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