Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Chapter 9 Conclusions and Further Research9.1. IntroductionThis chapter presents the conclusions for each research question initially presented in Chapter 1. Themain contributions of the thesis are shown for the theory and practice in the use of pattern recognitionand data mining techniques for generating risk prediction models. The limitations of the research arediscussed in order to propose directions for further works.9.2. Concluding RemarksChapter 1 introduced the reasons for the use of pattern recognition and data mining techniques in thecardiovascular domain. From this, a list of questions was generated (Section 1.3 in Chapter 1) tooutline specific goals for this research. This section discusses the research findings in the light of thesequestions.9.2.1. How Able Are The Existing Systems In Dealing With RiskPrediction For Patients?It is not to deny that the POSSUM and PPOSSUM systems have a dramatic impact on risk predictionin the cardiovascular domain for the morbidity and mortality of patients. From the reduction to 12physiological factors and 6 operative and postoperative factors (Copeland et al, 1991), for each patient,the mortality and morbidity rates are easily calculated using Equations (2.1), (2.2), and (2.3) in Chapter2. However, these systems do not consider all possible attributes. The POSSUM equations concentrateon the attributes defined by Copeland et al (1991) to calculate the physiological scores and theoperative severity scores whereas other attributes might contribute to the prediction of cardiovascularrisk for patients. For example, according to Kuhan et al (2001), the list of attributes in Table 6.1 inChapter 6 is seen as significant in risk prediction for cardiovascular patients. Some of these are notused in POSSUM calculations, but they are correlated to the heuristic outcomes with the thesis modelsvia the mutual information evaluations. For example, “Heart disease” attribute is significant (Kuhan etal, 2001), and it is not being used in POSSUM calculations. This attribute is very much correlated tothe models CM3aD outcomes (its value (HD) is 0.51- see in Figure 8.2 in Chapter 8) according tomutual information evaluation. Hence, this attribute can be seen as a significant indicator in theprediction of patient risk. This is to be expected as the POSSUM is a generic medical risk prediction139