Table 2.

Brief Definitions and Explanations of Artificial Intelligence Terminology

	Logistic Regression	LASSO	XGB	Neural Network	Deep Learning	Random Forest
Definition	Statistical analysis to predict a binary outcome based on prior observations of a data set. Model predicts a dependent data variable by analyzing the relationship between 1 or more independent variables	Shrinkage and variable selection method for linear regression models. Goal to obtain the subset of predictors that minimized prediction error for a quantitative response variable	Machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.	Series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates	Neural network that consists of multiple layers of interconnected nodes, each building upon a previous layer to refine and optimize the prediction or categorization	Supervised machine learning algorithm, as it builds decision trees on different samples and takes their majority vote for classification and average in case of regression

Abbreviations: LASSO, least absolute shrinkage and selection operator; XGB, eXtreme Gradient Boosting.