This is a homework question where I have not been able to reach any conclusion. I have an exam tomorrow. Please help me out.
We have a set of data from patients who have visited a hospital. A set of features (e.g., temperature , height...) have been also extracted for each patient. Our goal is to decide whether a new visiting patient some $n$ number of diseases or not.
This problem is to be solved using neural network. We have two choices: either to train a separate neural network for each of the diseases or to train a single neural network with one output neuron for each disease, but with a shared hidden layer. Which method do you prefer? Justify your answer .
My opinion: Both methods are almost the same because we have to train the same number of weights in both case. But in one case we can train them all at once and in another we will train them individually so for training it will take more time.