Will threshold of logistic regression change accuracy? Any relationship with the incidence of disease? I am using a logistic regression model to predict breast cancer. I trained and tested the model in a population with a pretty high incidence of breast cancer(since the individuals all went to the hospital and got tested), like 30% of the people have cancer. But then it hit me that what if I use this model in a population where the incidence of breast cancer is low--including people who underwent the screening but turned out to be healthy(they were excluded based on some criteria in this research). Will the accuracy of the model get decrease? Will it make my result look better if I increase the threshold of the logistic model? I'm trying to defend myself in terms of this issue and I really need some ideas and help.
To sum up, my question is whether the model accuracy has anything to do with the incidence of cancer (whether there is any evidence to defend that the model accuracy will remain decent in a population with a low incidence of disease), and is there any way to make my result look better?

That criticism of your approach is valid. Think of it this way: if the model thinkss that breast cancer is fairly common, it will not be so skeptical about someone having breast cancer.
We can think in terms of Bayes' theorem, where the logistic regression models $P(Y=1\vert X=x)$.
$$
P(Y=1\vert X=x) = \dfrac{P(X=x\vert Y=1)P(Y=1)}{P(X=x)}
$$
As you change the disease incidence (prior probability), $P(Y=1)$, you change the posterior probability, $P(Y=1\vert X=x)$.
