Bayesian Network Classifier for Medical Data Analysis

  • Beáta Reiz Biological Research Center, Central Labs, Bioinformatics Group 62 Temesvári krt., HU-6701, Szeged, Hungary E-mail:
  • Lehel Csató Babes Bolyai University, Faculty of Mathematics and Computer Science 1 Kogalniceanu str. RO-400084 Cluj-Napoca, Romania

Abstract

Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical – i.e. binary – data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record.Surgery survival prediction was examined with the algorithm. Bypass surgery survival chance must be computed for a given patient, having a data-set of 66 medical examinations for 313 patients.

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Published
2009-03-01
How to Cite
REIZ, Beáta; CSATÓ, Lehel. Bayesian Network Classifier for Medical Data Analysis. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 4, n. 1, p. 65-72, mar. 2009. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2414>. Date accessed: 16 july 2020. doi: https://doi.org/10.15837/ijccc.2009.1.2414.

Keywords

Bayesian networks, classification, medical data analysis, causal discovery