J Med Syst - Design ensemble machine learning model for breast cancer diagnosis.

Tópicos

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{ framework(1458) process(801) describ(734) }
{ data(3008) multipl(1320) sourc(1022) }
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{ use(1733) differ(960) four(931) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

Resumo Limpo

paper classifi breast cancer medic diagnost data inform gain adapt featur select neural fuzzi nf knearest neighbor knn quadrat classifi qc singl model scheme well associ ensembl one develop classif addit combin ensembl model three scheme construct valid experiment result indic ensembl learn perform better individu singl one moreov combin ensembl model illustr highest accuraci classif breast cancer among model

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