Induction of similarity measures and medical diagnosis support rules through separable, linear data transformations

To improve the medical diagnosis support rules based on comparisons of diagnosed patients with similar cases (precedents) archived in a clinical database. The case-based reasoning (CBR) or the nearest neighbors (K-N) classifications, which operate on referencing (learning) data sets, belong to this scheme.

Inducing similarity measure through special linear transformations of the referencing sets aimed at the best separation of these sets. Designing separable transformations can be based on dipolar models and minimization of the convex and piecewise linear (CPL) criterion functions in accordance with the basis exchange algorithm.

Separable linear transformations allow for some data sets to decrease the error rate of the K-N classification rule based on the Euclidean distance. Such results can be seen on the example of data sets taken from the Hepar system of diagnosis support.

Medical diagnosis support based on the CBR or the K-NN rules can be improved through separable transformations of the referencing sets.