Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Alternatively, a selection of the data set derived from the Hull site is stored in the “POSSUM,PPOSSUM Data Warehouse” for use with the POSSUM and PPOSSUM systems. The risk results areselected and combined with an appropriate data set in the Hull site. This combined data set is then usedto produce the scoring risk models for use in the later steps.HullsiteDundeesiteSelection .POSSUM,PPOSSUMPOSSUM,POSSUPPOSSUMM,PPOSSUMDataClean/Selection WarehouseData WareTransform-house FilterScoringrisksDecisionDecisionRule DecisionRuleWarehouse RuleWarehousesWarehouseRatioevaluationStandardmeasuresSOM,SOM, SOM, Unsupervised DataClustersKMIX,SOM,KMIX, KMIX, Data warehouseKMIX,of RiskWKMIX MI WarehouseWKMIX WKMIXWKMIXPredictionMLP, SupervisedMLP,Individua MLP,RBF, DataRBF,l Risk RBF,SVM WarehouseSVMPredictio SVMnFigure 5.4: The detailed steps for the thesis experiments.UnsupervisedFiltering taskSupervisedFilteringtaskStep 2 (Clean/Transform/Filter): Data is cleaned and transformed by using the appropriate datamining methods as detailed in the “Data Preparation Strategy” section below. Alternative models arethen built. Note that the term model here means the filtering data set (from the filtering task) derivedfrom the use of heuristic clinical decision rules. The three types of models introduced in Chapter 6 are:Clinical Models: These are based on the decision rules of significant attributes in the datadomain. For example, the clinical models CM3a, CM3b, CM4a, and CM4b, are based onsignificant attributes derived from clinical advice (Kuhan et al, 2001).69