Abstract
The paper highlights an approach to solving problems of medical diagnosis. The problems are formulated in terms of pattern recognition theory. An original algorithm is proposed. The combined information in the form of objects and rules is used for its training. Indirect justification of the algorithm with the use of the resolution method is presented. Keywords : pattern recognition, algorithms, resolution method, Diagnosis problems, decision support systems. DOI : 10.7176/CEIS/10-1-03
Highlights
Diagnosis problems often occur when describing different AI systems
One can discuss for a long time that algorithms solving the stated problem are inductive both by the nature of information and principles of their construction [3, 6]
In the following we will consider some properties of the constructed algorithms
Summary
Diagnosis problems often occur when describing different AI systems. they are the main body of systems oriented to management decision support. The majority of diagnosis problems in there essences have the following statement: In a certain set of objects X of arbitrary nature the following data is specified: finite number a of subsets (classes) X 1 , ..., X l (l N ). As for problem statement, a space of the formation of objects X Bn2 and a way of specifying initial information by rules were revised. With the restriction (3) and condition that objects can be described in space B2n and the initial information is specified by rules in the language of statement calculus in such a way that (1) is satisfied. When using parameters we may expect to obtain some additional "good" properties of algorithms, e.g. convergence, etc It is precisely this reasoning that determined the approach to problem solution described below
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