Neural networks approach to early breast cancer detection

Efficient prevention is strongly correlated with an early detection of breast cancer. The common effort of many researchers in this field resulted in the selection of the most prominent risk factors related to breast cancer. In this paper we present a neural network based model for the efficient automated identification of women at high risk of developing breast cancer from a wide, healthy population on the basis of data, referring to a properly selected set of risk factors and symptoms. Using this model we achieved a highly accurate classification and also the initial set of features reduction.

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