Figure 1: Schematic of the artificial neural network (ANN) constructed here.

Seventeen input variables were compared, converging on more than 100 training nodes (less training nodes were shown for simplicity). Each input variable connects, analogous to projections in neurons, to each training node. Arbitrary “weights” are then applied to each variable. Each training node is then used to determine the best “weights” of each variable to predict the outcome of interest (“target layer”).