Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models.
Journal:
Computer methods and programs in biomedicine
Published Date:
Oct 28, 2021
Abstract
BACKGROUND AND OBJECTIVE: Machine learning and deep learning models are very powerful in predicting the presence of a disease. To achieve good predictions, those models require a certain amount of data to train on, whereas this amount i) is generally limited and difficult to obtain; and, ii) increases with the complexity of the interactions between the outcome (disease presence) and the model variables. This study compares the ways training dataset size and interactions affect the performance of those prediction models.