Self-learning Bayesian Networks in Diagnosis | Zendy

Petr Suchánek | Zendy; Franciszek Marecki | Zendy; Robert Bucki | Zendy

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Self-learning Bayesian Networks in Diagnosis

Author(s) -

Petr Suchánek,

Franciszek Marecki,

Robert Bucki

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.08.200

Subject(s) - computer science , bayesian network , probabilistic logic , artificial intelligence , bayesian probability , machine learning , basis (linear algebra) , knowledge base , bayesian statistics , statistical model , base (topology) , probabilistic classification , data mining , bayesian inference , naive bayes classifier , mathematics , mathematical analysis , geometry , support vector machine

The article presents the main bases of artificial intelligence, probabilistic diagnostic methods, development of the diagnostic database and diagnostic base of knowledge and Bayesian networks as a base of the diagnostic self-learning systems which are commonly used in medicine to recognize diseases on the basis of symptoms. Probabilistic models of diagnostic networks are based on the Bayesian formulas. These formulas let us determine probabilities of causes on the basis of probabilities of results. This is the reason why databases must be created and adequate probabilities determined. Results of this research are then analyzed by means of statistical methods

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