Self-learning bayesian networks in diagnosis

被引：10

作者：

Suchanek, Petr ^{[1
]}

Marecki, Franciszek ^{[2
]}

Bucki, Robert ^{[3
]}

机构：

[1] Silesian Univ Opava, Sch Business Adm Karvina, Univ Namesti 1934-3, Karvina 73340, Czech Republic

[2] Wyzsza Szkola Biznesu Dabrowie Gorniczej, PL-41300 Dabrowa Gornicza, Poland

[3] Inst Management & Informat Technol, PL-43300 Bielsko Biala, Poland

来源：

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 | 2014年 / 35卷

关键词：

artificial intelligence; self-learning Bayesian networks; medical diagnostic; diagnostic systems; databases; simulation; SYSTEM;

D O I：

10.1016/j.procs.2014.08.200

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2014 The Authors. Published by Elsevier B.V.

引用

页码：1426 / 1435

页数：10

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