Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Step 1 (Selection): The data set is selected from the Dundee site with 18 attributes, and 341 cases.Step 2 (Clean/Transform/Filter):Cleaning task: Missing values are filled using the same method as in the previous section.Transformation task: This task requires only numerical attributes. Hence, the continuous datais rescaled into the range of [0, 1] using the linear equation as indicated above. The other(categorical and Boolean) attributes are ignored in this step.Filtering task: The following heuristic decision rules are applied based on the two attributes“PATIENT STATUS” and “COMBINE”. The model CM3aD has two levels of risks (“Highrisk”, “Low risk”) as given by:(PATIENT STATUS, COMBINE) = 0 “Low risk”(PATIENT STATUS, COMBINE) 1 ”High risk”The model CM3bD has three levels of risks (“High risk”, “Medium risk”, “Low Risk”) as given by:(PATIENT STATUS, COMBINE) = 0 “Low risk”(PATIENT STATUS, COMBINE) = 1 ”Medium risk”(PATIENT STATUS, COMBINE) = 2 ”High risk”Step 3 (Data Mining Techniques): The clustering algorithm KMIX is used in this step with bothmodels without the expected outputs indicated above. The number of required clusters is 2 and 3according to the model CM3aD and CM3bD respectively.Step 4 (Compared/ Evaluation): The clustering results are compared to the expected outcomesdefined in step 2 by using standard measures such as confusion matrix, sensitivity, specificity rates, andso on.Step 5 (Building Clustering Models): The new clustering models of CM3aDC and CM3bDC are builtbased on the KMIX results. This means the input set is the same as in the models of CM3aD andCM3bD. However, these new models‟ outcomes are derived from the KMIX results. Both new models,CM3aDC and CM3bDC, are then applied again from step 3 in the thesis framework. A new process iscreated for a request for the use of supervised neural network techniques. The results are then measuredand evaluated with standard measures.77