TABLE 1.

Common Supervised Machine Learning Algorithms

Algorithm Types	Brief Description
Decision tree	An ML model in the form of a tree with a sequence of nodes which are either a decision associated with particular values of a variable or dependent variable
Logistic regression	A statistical model that uses a logistic function to model a binary dependent variable
Naive Bayes	An ML model based on applying Bayes’ theorem with the naive assumption of conditional independence between all pairs of independent variables given the dependent variable
Support vector machines	An ML model that classifies instances by creating an optimal boundary between variables after mapping them to a higher dimensional space
Ensemble methods	A set of methods that use multiple learning models to obtain better predictive performance than could be obtained from any of the constituent models alone
Random forest	An ensemble learning method that employs multiple, even hundreds or thousands, of decision trees as its constituent models
Neural networks	Also known as deep learning, inspired by biological neural networks in humans and animal brains. These models can consider the sequence of clinical events in predictive modeling