Predicting Cardiovascular Risks using Pattern Recognition and Data ...

x 2Decisionboundary++++++++++++ + ++ +Low riskHigh riskFigure 3.7: An example of a nonlinear problem.x 13.4.3. Linear Vs Nonlinear ModelsA comparison of the linear and nonlinear models can be seen in Table 3.4.It is clear that linear models are the first choices whenever a new model is generated. However, theystruggle to deal with the classification of noisy data whereas nonlinear classifiers usually deal betterwith noisy data (Manning et al, 2008). Furthermore, according to NIST/SEMATECH (2006), linearmodels have limited shapes, because they only can be devised using linear functions. This might causea poor performance for their classification process. On the other hand, nonlinear models can be usedwith a broad range of linear and nonlinear functions. Therefore, they might demonstrate betterclassification performance than linear models.For example, the linear scoring model of Gupta et al (2005) introduced in Chapter 2 uses a global linearfunction to produce the cardiovascular risk for the patients. However, its performance might be poorwith noisy classification problems as indicated by the disadvantages of linear models in Table 3.4. TheINdividual Data ANalysis of Antihypertensive (INDANA) intervention trials (Pocock et al, 2001) aswell as the POSSUM and the PPOSSUM systems introduced in Chapter 2 use local linear functions tocalculate the system scores. These scores are then used with nonlinear functions, which are derivedfrom the logistic regression, to produce individual numerical risks. Therefore, they show advantages indealing with noisy classification in the cardiovascular risk prediction as indicated in Table 3.4.Similarly, neural network classifiers such as multilayer perceptron, radial basis function, and support31