Improved classification of psychiatric mood disorders using a feedforward neural network

Our aim was to use a neural network model for classifying psychiatric mood disorders. In approaching this interdisciplinary task, the concern was not to build a more biologically plausible model, but to test the computational capabilities of an existent one. The multilayer perceptron was chosen as a model, trained and tested using both theoretical and real cases. The classification proved to be good, but we are aware of the fact that both training and testing should be performed in the future only on real data, as the way to get a true picture of the system's performance.