Forecasting: Adopting the Methodology of Support Vector Machines to Nursing Research

In nursing studies, linear statistical methods are commonly used to analyze data on subjective perceptions. Most of the collected data include participants’ subjective perceptions and are usually not collected in a controlled environment. For linear regression analyses, there is a common understanding that the distributions of some studied variables might not be close to linear in nature. However, when using the support vector machine (SVM), there is no violation concern for the regression assumption of linearity. SVM, as an artificial intelligence learning method, is based on the structural risk minimization principle that relies on the computational learning theory. It is a powerful technique for nonlinear regression or pattern recognition, which can learn complex patterns or trends from input data (independent variables) and create outputs (dependent variables). A trained learning machine can then generate output for unseen data. The quality of SVM was determined by the machine’s training error rate and generation capability. Choosing the approaches of nonlinear regression or pattern recognition is based on the characteristics of the dependent variable (Cristianini & ShaweTaylor 2000; Figure 1). How does SVM differ from the statistical methods? How could nursing researchers adopt SVM to address nursing issues? In this commentary, these two questions are answered. The process of developing SVM and three example studies using SVM are introduced in the following.