Regression Routines for Breast Cancer Prediction using Multilayered Neural Network
Abstract
Presently breast cancer is spiking out as the 2nd most dangerous cancer in the world. Breast cancer has turned out in a wider range and is a known pandemic as it occurs in age aspects of women from adults to old ones. The malignant tumor is basically the main cause and which is evn hard to identify with mammography as well as human discretion. Thus, advancement over the technological researchers have actually become a winning factor as machine learning and artificial neural networks which are helping the researchers and doctors in worldwide range. The valuation of predicting is being done by the usage of algorithms such as logistic regression and softmax regression to train the models with data set wisconsin breast cancer date of UCI machine learning repository. To perform all of these operation we make use of Google colab (Jupyter notebook backed by python 3.x). The proposed work results into the trained neutral network model which is having much better accuracy while compared to other pre-existing models.
