Predicting Breast Cancer with Deep Neural Networks

Abstract

In this study, a deep neural network (DNN) MODEL was developed which diagnoses breast cancer using information about age, BMI, glucose, insulin, homa, leptin, adiponectin, resistin and MCP-1. The data used in this model was collected by Patricio et al. [7] from 116 women of which 64 has breast cancer and 52 do not. While 70% of this data (81 cases) was used for instructing the DNN model, 30% (35 cases) was used for testing. The DNN model was created in Python programming language using Keras Deep Learning Library. After model creation, machine learning was conducted using probable optimisation algorithms, loss functions and activation functions and the best three models were saved. For performance evaluation of the models, metrics of specificity, sensitivity and accuracy were employed. The specificity values of the best three models were calculated as [0.882, 0.941] and sensitivity values were found to be [0.888, 0.944]. In other words, while the models predict healthy women at the rates of minimum 88.2% and maximum 94.1%; they predict women with breast cancer at the rates of minimum 88.8% and 94.4%. For both women with and without breast cancer these prediction rates are sufficient and much higher than those reported by Patricio et al. [7].