One way to tell if a Machine Learning model is any good is to see how it does on unseen/new patients.

Of course, we don't wait to try it on real patients, so typically you'd partition the data you have already into (a) what you show to the machine learner (training data), and (b) what you hide from the learner (test data). The latter is only used to evaluate, i.e. you get the answer from the ML model and compare it to the real answer you have already. If information about the test data some how makes it to the training data, its referred to as a mining leak [1].

In this paper, they treat each month of a patient as an independent observation. However, GPS driver behaviour will be very similar from one month to the next for the same person. Genetic information is exactly the same. So for every month that the model is tested (test data), the learner has already seen very similar data in the training set - for some of the other months (for the same person) that happen to be in the training set. The split is typically done randomly. So it will do well.

The test results are therefore optimistic and do not support the conclusions.

[1] https://en.wikipedia.org/wiki/Leakage_(machine_learning)

Suppose that you have 3 data points, on June 14, 15, and 16, that due to personal driving quirks all appear to belong to the same person. If the 14th and 16th are in your dataset, and both correspond to Alzheimer-free Bob, that may be a strong hint that the data from the 15th is also Alzheimer-free.

But this doesn't help you in the real world where you won't necessarily have near neighbors corresponding to the same person, with a known diagnosis.

Overfits for study participants. Will Not necessarily give same results on gen pop