Artificial Neural Networks are algorithms that are inspired by neural signaling in the human brain. Deep learning is a subset of machine learning methods that utilizes artificial neural networks and feed-forward neural networks are a common artificial neural network that is applied when using deep learning methods. Feature selection works by selecting features that are relevant for classification and thus, allows for easier interpretation of the model as well as reduction of overfitting. The aim of this study was to investigate the impact of feature selection on the performance of the model. This was achieved by performing a binary classification analysis, where the overall five year survivial of patients that suffered from breast cancer was predicted. A deep feed forward neural network model was trained to differentiate between “alive” and “deceased” patients. As a part of the procedure, different feature selection methods were used to observe the effect that they had on the model performance. According to the results, using feature selection with mutual information, variance filter and PCA had an impact on the deep FFNN model performance, as compared to the correlation based method, which was significantly different in the model performance. In conclusion, feature selection with mutual information, variance filter and PCA lead to a better model performance, both in the discriminative power as well as the overall ability to make correct predictions as compared to correlation-based feature selection, which was proven to provide a poor model performance.