Comput. Biol. Med. - Breast-cancer identification using HMM-fuzzy approach.

Tópicos

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Resumo

This paper presents an ensemble of feature selection and classification technique for classifying two types of breast lesion, benign and malignant. Features are selected based on their area under the ROC curves (AUC) which are then classified using a hybrid hidden Markov model (HMM)-fuzzy approach. HMM generated log-likelihood values are used to generate minimized fuzzy rules which are further optimized using gradient descent algorithms in order to enhance classification performance. The developed model is applied to Wisconsin breast cancer dataset to test its performance. The results indicate that a combination of selected features and the HMM-fuzzy approach can classify effectively the lesion types using only two fuzzy rules. Our experimental results also indicate that the proposed model can produce better classification accuracy when compared to most other computational tools.

Resumo Limpo

paper present ensembl featur select classif techniqu classifi two type breast lesion benign malign featur select base area roc curv auc classifi use hybrid hidden markov model hmmfuzzi approach hmm generat loglikelihood valu use generat minim fuzzi rule optim use gradient descent algorithm order enhanc classif perform develop model appli wisconsin breast cancer dataset test perform result indic combin select featur hmmfuzzi approach can classifi effect lesion type use two fuzzi rule experiment result also indic propos model can produc better classif accuraci compar comput tool

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