Abstract
In many medical decision problems there exist dependencies between subsequent diagnosis of the same patient. Among the different concepts and methods of using “contextual” information in pattern recognition, the approach through Bayes compound decision theory is both attractive and efficient from the theoretical and practical point of view. Paper presents the probabilistic approach (based on expert rules and learning set) to the problem of recognition of state of acid-base balance and to the problem of computer-aided anti-hypertension drug therapy. The quality of obtained classifier are compared to the frquencies of correct classification of three neural nets.
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Keywords
- Posterior Probability
- Learning Sequence
- Pattern Recognition Algorithm
- Decision Area
- Conditional Density Function
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Wozniak, M. (2005). Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_24
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DOI: https://doi.org/10.1007/11573067_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
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