Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Table C19: Experimental results of CM3b and CM4b models.C.5.3. Scoring Risk modelsStep 1 (Selection): The data is selected from the Hull site and the POSSUM and PPOSSUM results.The models are built as Mortality, Morbidity, and Death rate. Note that these models share the samestructure (see in Table C20) including 499 cases and 22 input attributes.Step 2 (Clean/Transform/Filter):Cleaning task: The method of filling missing values is the same as experiments above. Thismeans, for example, continuous missing values are replaced by the mean of non-missingnumerical values. Note that the second column in Table C20 shows number of missing valueswhereas the last column shows replacing values if applicable. For example, the missing valuesin the attribute WCC are replaced by the mean (7.67).Transformation task: This task is to transform all data to numerical data type. This means thenumerical data is rescaled in to the range of [0,1]. Boolean values are transformed into values of0 or 1. The categorical data is transformed into discrete Boolean (“Normal” and “Abnormal”)then they are transformed into values of 0 or 1.Filtering task: Some attributes in these models structure can be eliminated. They are JVP, GCS(Coma Score), URGENCY, OP-SEVERITY, MALIGNANCY, and PERI_SOILING (see thesummary in Table C20). For example, the attribute namely JVP contained only value of “N”, orMALIGNANCY contained only value of “None” as well. Hence, the data sets contain now 16input attributes and 497 cases.The outcome for three models are calculated basing on the average (mean) values of Mortality,Morbidity, and Death rate scores as follows:.Mortality model:IF Mortality>= meanOtherwise “Low risk” “High risk”Morbidity model:IF Morbidity>= mean “High risk”189