Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Table 6.14 Alternative number of cross-validation experiments. 122Table 6.15 SOM-Clustering results for CM3bD model 126Table 6.16 Clustering results for CM3aD model 129Table 6.17 Clustering results for CM3bD model 129Table 6.18 Neural network results for CM3aDC model 130Table 6.19 Neural network results for CM3bDC model 130Table 6.20Neural network and POSSUM and PPOSSUM sensitivitiescomparison131Table 7.1 Experimental results of CM1 and CM2 models 136Table 7.2 Experimental results of CM3a and CM4a models 138Table 7.3 Experimental results of CM3b and CM4b models 140Table 7.4 Confusion matrix for scoring risk models 141Table 7.5 The clustering results for model CM3a 143Table 7.6 The clustering results for model CM3b 143Table 7.7 The CM3aC model results 145Table 7.8 The CM3bC model results 145Table 7.9 Results of first group‟s classifiers 148Table 7.10 Results of random classifiers for first group‟s models 149Table 7.11 The average classification rates of subgroups models 151Table 7.12 The average classification rates of clustering models (second group) 151Table 7.13The distances from expected classes to alternative clusteringoutcomes153Table 7.14 The distances between alternative groups in confusion matrix 154Table 7.15The confusion matrix for CM3a model with all supervisedclassifiersxi155