Classification of Breast Cancer and Determination of Related Factors with Deep Learning Approach (original) (raw)

2020

Abstract

Aim: In this study, it is aimed to classify breast cancer and identify related factors by applying deep learning method on open access to breast cancer dataset. Materials and Methods: In this study, 11 variables related to open access to breast cancer dataset of 569 patients shared by the University of Wisconsin were used. The deep learning model for classifying breast cancer was established by a 10-fold cross-validation method. The performance of the model was evaluated with accuracy, sensitivity, specificity, positive/negative predictive values, F-score, and area under the curve (AUC). Factors associated with breast cancer were estimated from the deep learning model. Results: Accuracy, specificity, AUC, sensitivity, positive predictive value, negative predictive value, and F-score values obtained from the model were 94.91%, 91.47%, 0.988, 96.90%, 95.42%, 95.14%, and 96.03%, respectively. In this study, when the effects of the variables in the dataset on breast cancer were evaluate...

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