ArticleCroatian medical journal2021
Improving breast cancer prediction using a pattern recognition network with optimal feature subsets.
Article in Croatian medical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 6 citations in OpenAlex.
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1 author at 1 institution in 1 country.
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Abstract
aimTo predict the presence of breast cancer by using a pattern recognition network with optimal features based on routine blood analysis parameters and anthropometric data.
methodsSensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and Fowlkes-Mallows (FM) index of each model were calculated. Glucose, insulin, age, homeostatic model assessment, leptin, body mass index (BMI), resistin, adiponectin, and monocyte chemoattractant protein-1 were used as predictors.
resultsPattern recognition network distinguished patients with breast cancer disease from healthy people. The best classification performance was obtained by using BMI, age, glucose, resistin, and adiponectin, and in a model with two hidden layers with 11 and 100 neurons in the neural network. The accuracy, sensitivity, specificty, FM index, and MCC values of the best model were 94.1%, 100%, 88.9%, 94.3%, and 88.9%, respectively.
conclusionBreast cancer diagnosis was succesfully predicted using only five features. A model using a pattern recognition network with optimal feature subsets proposed in this study could be used to improve the early detection of breast cancer.
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Identifiers
34730888PMC8596469W3209888445What OpenQuestion holds
Registered trials
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