ABSTRACT
Biomedical problems can be broadly divided into three types—optimization of a predefined objective function, static classification problems, and predictive models. The first type of problems consists of trade-offs between different algorithmic factors that are reasonably well defined. The second type of algorithms is extremely typical—the function used for classification purposes is unknown, but the data and the expected discrete outcomes are known, and the objective is to determine a classification function that would accurately map the initial data set to those observations. Finally, the last type of problems involves the prediction of the value of random variable/s, based on a set of observed features. This chapter provides an overview in terms of the problem types and how each can be practically formulated. It focuses on a framework, allowing problems and their constraints to be formulated clearly. The chapter also provides four case studies involving different types of problems.
