Predicting Cardiovascular Risks using Pattern Recognition and Data ...

3.3.3. Syntactic Pattern ApproachThe syntactic pattern approach is based on concepts from formal language theory, and in particularmathematical models of grammar (Gonzalez and Thomason, 1978). Syntactic pattern recognitiondecomposes the patterns into sub-patterns or primitives. The goal is to classify each pattern asbelonging to a specific class. The decomposition of patterns is sometimes referred to as parsing.Schalkoff (1992) suggested two approaches: top down parsing, and bottom up parsing. The syntacticpattern recognition approach can be used for the classification and description purposes.The elements of classification process are shown in Figure 3.3 (Schalkoff, 1992). This approach hasdisadvantages in implementation if the data set includes noisy patterns. This is because of difficulties indetecting the primitives, and in the inference of the grammar. Moreover, the explosion of combinatorialpossibilities requires a large training data set and much computational effort (Perlovsky, 1998). Moredetail about this approach can be seen in Fu (1982) and Schalkoff (1992). Another view of syntacticpattern approach can be seen in the “Decision Trees” section.„Library‟ ofclasses,categorizedby structureClass 1structureClass 2structure•••Class cstructureInputStructuralanalysis(Structural)MatcherRelevantMatch(es)Figure 3.3: Using syntactic pattern approach for classification (Schalkoff, 1992).3.3.4. Neural Network Pattern RecognitionThe general background to neural networks is introduced in this section. Artificial neural networks(neural networks) have a rich history of research, starting with the McCulloch and Pitts (1943) conceptof neural networks, and the following popular Hebbian rule (Hebb, 1949). From the first concept ofperceptron (Rosenblatt, 1958; Rosenblatt, 1962; Minsky & Papert, 1969), neural networks havedeveloped quickly and been applied in many areas. The three characteristic components of a neuralnetwork can be seen as:The network topology, or interconnection of neural „units‟;The characteristics of individual units or artificial neurons;25