I have not found any resource on how to use a neural network to model measurements from a repeated-measures design. Let's say we have a small dataset (for example, 14 independent variables, 15-repetitions, and 50 subjects) of measured continuous variables as independent variables, plus some constant-valued independent variables such as age and sex. We also have one dependent variable measured at each repetition. I have tried to use a feedforward neural network with 10 neurons in one hidden layer to model this, and one-subject-out cross validation, but I think it can be improved maybe by following techniques: 1. Discretization of continuous input variables, since the dataset is small. 2. Using a feedback in training the network since a dependent variable (output) for each subject could be affected by the output from preceding outputs. 3. Remove redundant input variables using feature selection approaches.
The main problem is that I do not know how to input the constant-valued variable (age) to this model. And how to put this feedback into the model. Finally if I got the model accurate enough in one-subject-out validation, is it fine to average the weights and test this new model on the dataset and if it was good enough use the model for unseen data?