Predicting Cardiovascular Risks using Pattern Recognition and Data ...

equivalent rates for “High risk” are very poor (0.93; 0.84 vs 0.09; 0.19 on average). The two radialbasis function classifiers, CM1-RBF and CM2-RBF, predicted all expected “High risk” patients as“Low risk”. This again shows the technique‟s disadvantages for use with the thesis data.Confusion MatrixClassifiersRiskHigh riskLow riskACC Sen Spec PPV NPVCM1-MLPCM1-RBFCM1-SVMCM2-MLPCM2-RBFCM2-SVMHigh risk 9 117Low risk 34 679High risk 0 126Low risk 0 713High risk 30 96Low risk 112 601High risk 6 133Low risk 27 673High risk 0 139Low risk 0 700High risk 24 115Low risk 125 5750.82 0.07 0.95 0.21 0.850.85 0.00 1.00 N/A 0.850.75 0.24 0.84 0.21 0.860.81 0.04 0.96 0.18 0.830.83 0.00 1.00 N/A 0.830.71 0.17 0.82 0.16 0.83Table 7.1: Experimental results of CM1 and CM2 models.From Table 7.1, the correct predicted “High risk” rates (sensitivity as well as the positive predictivevalue) are very poor (0.09 and 0.19 on average except CM1-RBF and CM2-RBF). The nature of theproblem and the difficulty of measuring influential parameters might be the cause for these poorperformances.7.2.2. Clinical Models CM3a and CM4aTable 7.2 shows the results for supervised neural networks on the clinical risk prediction models CM3aand CM4a. These models share the same expected outputs but their input sets are different (as indicated108