Predicting Cardiovascular Risks using Pattern Recognition and Data ...

CM1 and CM2 can be seen in Table C15 below. The same as other experiments, the data is dividedinto training set (90% of population), test set (10%) with a number of cross-validation folds of 10. Themultilayer perceptron technique is chosen with the topology of 25-2-1 (25 input nodes; 2 hidden nodes;1 output -2 class nodes); learning rate is 0.3; and number of epochs is 500. Radial basis functionclassifier has centre parameter c of 2. The support vector machine uses poly kernel function with theexponent parameter p of 2.Step 4 (Comparison/ Evaluation): The results are presented in confusion matrix for the use ofstandard measurements of sensitivity, specificity, and so on (see Table C15).ClassifiersCM1-MLPCM1-RBFCM1-SVMCM2-MLPCM2-RBFCM2-SVMRiskConfusion MatrixHighriskLow riskHigh 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 575ACC Sen Spec PPV NPV0.82 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 C15: Using neural network techniques for model CM1 and CM2.C.5.2. Clinical ModelsModels CM3a, CM3b, CM4a, and CM4bStep 1 (Selection): Models CM3a and CM3b contain 18 attributes (16 input attributes and 2 foroutcome calculations) and 839 patient records (see in Table C16 below). Models CM4a and CM4bcontain 16 attributes (14 input attributes and 2 for outcome calculation) and 839 patient records (see inTable C17).Step 2 (Clean/Transform/Filter):184