To further explain my question, I will explain my use-case. Say I have a model which is trained for how good/bad a food is for obesity based on its nutrition facts. And another model for, say hypertension. I wish to combine these models to be able to predict food which are good for a person suffering from both obesity and hypertension.
I do not wish to retrain a new model for both cases as eventually I will add more diseases and do not wish to train for each combination of diseases.
Ideally I would like to be able to combine different models (eg Logistic Regression, SVM) but as a stepping stone, I was looking to combine two models of the same type.
I am not sure if Stacking would be appropriate in this case.