Predicting Cardiovascular Risks using Pattern Recognition and Data ...

More detail about these applications can be seen in Ripley (1996); and Lisboa (2002). The use ofpattern recognition techniques is to somehow mimic the decision making of humans. According to Touand Gonzalez (1974), the fundamental problems in building a pattern recognition system are: The sensing problem; The pre-processing and feature extraction problem; And the determination of optimum decision procedures, which are needed in the identificationand classification process.In the sensing problem, the pattern recognition system is concerned with the representation of inputdata. This representation should be chosen to aid the measurement of similarity between the currentobject and previously recognized classes. In the second problem, the system is concerned with theextraction of characteristic features or attributes from input data and the reduction of the dimension ofpattern vectors. In the third problem, the data will be formed as pattern points or measurement vectorsin feature space. By using alternative classified techniques, the system will decide to which classesthese data belong. The architecture of an archetypal pattern recognition system can be seen in Figure3.1.Possible algorithm feedback or interaction(Statistic)Observedworldpatterndata piSensor/transducerPreprocessingandenhancementFeature/primitiveextractionalgorithmClassificationalgorithm(Syntactic)DescriptionalgorithmClassificationDescriptionMeasurement, miFigure 3.1: Typical pattern recognition system architecture (Schalkoff, 1992).As an example of this pattern recognition process, cardiovascular diagnosis can be seen as follows: Theoriginal collected data is represented in the form of database files; data might be then reduced orcleaned by using alternative data mining techniques; by using feature exaction algorithms, the featurespace dimension might be reduced; the system then uses classification algorithms or description21