CLASSIF1 Data Pattern Analysis

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< fig.1      fig.2: Reclassification of the Learning Set      > fig.3

- The disease mask for the reference group of patients (normals) contains typically a sequence of (0) characters because the majority (80%) of the parameter values are located between the two percentiles thresholds 10% and 90%, 10% below (-) the lower percentile and 10% above (+) the upper percentile.

- Unknown patients are classified according to the highest positional coincidence of the patient classification mask with any of the disease classification masks.

- The degree of coincidence between the patient mask and the best fitting disease mask is expressed by the mask coincidence factor. The coincidence factor is 1.00 for patients #10/14/16/17/18/19/23 but also for non risk (normal) patients #1/6/7 (arrows <= ) despite the fact that not all triple matrix characters are (0). Infarct risk patients have all discriminatory parameters of the disease mask increased (+), patients with normal (0) or diminished (-) discriminatory parameter values belong to the non risk (normal) patients. Parameter values (-) are therefore counted as hit for non risk (normal) patients, thus explaining the coincidence factor of 1.00 for patients #6/7.

- Displayed are the triple matrix patterns of the first 10 patients of the normals and the myocardial risk patients.

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