Predicting Cardiovascular Risks using Pattern Recognition and Data ...

a new patient. For example, t 5 cannot be given any output labels as it is only a partial match to anyexisting patient.This approach is not be used within this thesis and more detail can be seen in Schalkoff (1992).3.3.2. Statistical Pattern ApproachThis section demonstrates the use of statistical pattern approach for the prediction or classificationprocess. In the statistical pattern approach, each pattern is represented in terms of d- features ormeasurements. The main purpose of this approach is to choose suitable features in order to assignpattern vectors to different categories. If the patterns can be divided into separate classes, the featurespace will be well determined. At this point, there exists a decision boundary in the feature space toseparate these classes. Bishop (1995) defined the decision boundaries using a discriminant functionbased on Bayes‟ theory. Alternatively, these decision boundaries are built based on a classificationapproach (Jain et al, 2000). Detail about these methods can be seen in Bishop (1995); and Jain et al(2000). An example of a statistical pattern recognition model can be seen in Figure 3.2. The modelcontains two modes: the training (or learning) process; and the classification (or testing) process. In thepre-processing module, the pattern is segmented from the background, noise removed then normalized.For example, the pre-processing stage deals with missing values (cleaning task) and transformingoriginal valued types into more appropriate types (normalisation task).TestPatternPre-processingFeatureMeasurementClassificationClassificationTrainingTrainingPatternPre-processingFeatureExtraction/SelectionLearningFigure 3.2: Model for statistical pattern recognition (Jain et al, 2000).In the training stage, the “feature extraction or selection” module finds appropriate features for therepresentation of input patterns. The classifier is then trained to partition the feature space. In theclassification stage, the trained classifier assigns each input pattern to one of the pattern classes basedon the measured features.24