I have a training set of 50 subjects with about 550-600 measurements each. One measurement consists of 24 features and one class label (1 or 0). So my data looks like this (simplified):
Subject F1 F2 F3 ... Class
1 1 3 2 ... 1
1 1 4 7 ... 0
...
2 2 3 2 ... 1
2 1 1 1 ... 1
...
I want to train my classification model (artificial neural network) with all measurements of all 50 subjects. Now I want to know if I can just use leave-one-out-cross-validation on the subject level (training with all measurements of all but one subject and then testing on the measurements of the remaining subject) or is validation on measurement level also needed (k-fold cross-validation within the measurements of each single subject)?