Comput Methods Programs Biomed - A random forest classifier for lymph diseases.

Tópicos

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Resumo

Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.

Resumo Limpo

machin learningbas classif techniqu provid support decisionmak process mani area health care includ diagnosi prognosi screen etc featur select fs expect improv classif perform particular situat character high data dimension problem caus relat train exampl compar larg number measur featur paper random forest classifi rfc approach propos diagnos lymph diseas focus featur select first stage propos system aim construct divers featur select algorithm genet algorithm ga princip compon analysi pca relieff fisher sequenti forward float search sffs sequenti backward float search sbfs reduc dimens lymph diseas dataset switch featur select model construct second stage obtain featur subset fed rfc effici classif observ garfc achiev highest classif accuraci dimens input featur space reduc eighteen six featur use ga

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