Support Vector Machines

Support vector machines (SVMs) are machine learning models that share some similarities with neural networks and logistic regression models for classification tasks. Examples of such tasks arise naturally in clinical settings, whenever one is given a set of data descriptors (e.g., lab results, clinical findings, imaging data, genetic information) and wants to predict health status or medical outcome given such data. In the simplest case, which is discussed in this entry, there are only two possible outcomes to predict. In a clinical context, these two can, for example, correspond to healthy and diseased patient states, respectively.

Traditionally, logistic regression and artificial neural network models have been the tools of choice for solving classification tasks as outlined above. In the past 10 years, SVMs have increasingly been ...