Table 2.

Overview of Common Supervised Machine Learning Classification Algorithms

Algorithm	Description	Advantages and Disadvantages	Suitable Data in Cardiac Electrophysiology
Deep learning	Mimics human neuronal structure with many processing layers	Represents state-of-the-art performance with raw input data in complex tasks and does not require any feature engineering but often requires extremely large datasets, intensive computational power, significant processing time, and algorithms are difficult to interpret	Raw 12-lead ECG, imaging (CT, MRI, echocardiography), clinical text, or other diagnostic and monitoring data (telemetry, Holter/wearables, electrograms, electrophysiological studies)
Traditional supervised ML algorithms
Logistic regression	Linear combinations of log-odds	Transparent and fast but performs poorly with large numbers of variables and does not automatically capture interactions	Well-selected features obtained from the electronic medical record or manually engineered from processed data (eg, features extracted from ECG signal processing, specific measurements made on images, etc)
Support vector machine	Identifies hyperplane that separates classes, can use a linear or nonlinear kernel	Fast can be flexible with kernel adjustment but is difficult to interpret and features may need normalization and scaling
Naïve Bayes	Bayes’ theorem of conditional probabilities	Fast, scalable, does not require large amounts of data, but is not directly interpretable and assumes independence between variables
Random forest	Large ensembles of decision trees	Resistant to noise and overfitting even with large amounts of variables, flexible with continuous and categorical variables, capable of capturing variable interactions	High quantities of features obtained from the electronic medical record or manually engineered from processed diagnostic data (ECG, imaging, etc)

CT indicates computed tomography; ML, machine learning; and MRI, magnetic resonance imaging.