Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Both models CM3aC and CM3bC are used alternative neural network techniques. The topologies andparameters are the same as in model CM3a and CM3b experiments in section C.5.2 above. The resultscan be seen in Table C24 and C25.Classifiers Risk C4VH C2M C1L ACC Sen Spec PPV NPV MSECM3bC-MLP(MLP_2H_0.3_500)CM3bC-RBF(RBF_c=2)CM3bC-SVM(SVM_Poly_p=2)C4VH 302 1 1C2M 3 233 7C1L 1 3 288C4VH 300 2 2C2M 3 235 5C1L 4 5 283C4VH 304 0 0C2M 0 240 3C1L 0 12 2800.99 0.99 0.99 0.99 0.97 0.010.98 0.99 0.97 0.98 0.98 0.010.98 0.99 0.96 0.98 0.99 0.07C.6. Case Study VTable C25: The CM3bC model results with alternative neural network classifiers.Model CM3aD and Model CM2Step 1 (Selection): A selection data set of model CM3aD includes 18 attributes (16 input attributes and2 for outcome calculations) and 341 patient records (see in Table C4). Another selection data set ofmodel CM2 includes 26 attributes and 839 cases data derived from the Hull and Dundee sites (see inTable C14).Step 2 (Clean/Transform/Filter):Cleaning task: The missing values are treated as above experiments.Transformation task: All data is transformed to appropriate categorical types. Boolean valuescan be seen as categorical Boolean values of 0 or 1. The categorical values are ignored in thistask. The numerical data is transformed to categorical by discretization method (Venables andRipley, 1994; Yang et al, 2001; and Tourassi et al, 2001). This means continuous data is divided193