Predicting Cardiovascular Risks using Pattern Recognition and Data ...

From the confusion matrix in Table 3.5, the number of correct or incorrect (misclassification) patternscan be derived. The numbers along the major diagonal (from left to right) represent the correct whilethe rest represent the errors (confusion between the various classes).3.5.3. Performance measuresSome related concepts according to Altman and Bland (1994a; 1994b); Dunham (2002); Ye(2003);Han and Kamber (2006); Kononenko and Kukar (2007); and Bramer (2007) such as the accuracy(ACC), sensitivity (Sen), specificity (Spec) rates, and the positive predictive value (PPV or precision),and the negative predictive value (NPV) can all be built from the confusion matrix. These rates areused to evaluate and discuss classification performance.The accuracy (Duham, 2002; Ye, 2003; Han and Kamber, 2006; Kononenko and Kukar, 2007;Fielding, 2007; and Bramer, 2007) of a classifier is calculated by the total number of correctlypredicted “High risk” (true positive- true High risk) and correctly predicted “Low risk” (true negativetrueLow risk) over the total number of classifications. It is given by:TP TNACC TP FP TN FN(3.2)The error rate of performance, or misclassification rate, can be referred from this accuracy rate as: 1-ACC.Figure 3.8: Classification performance rates.However, the accuracy does not show how well the classifier can predict the positive (“High risk”) andthe negative (“Low risk”) for the classification process. Therefore, the sensitivity, specificity, positive34