Metastasis identification based on clinical parameters using Bayesian network

Arida Ferti Syafiandini, Ito Wasito

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Bayesian Network is a promising method for modelling probabilistic relationships among causally related variables. This paper presents an application of Bayesian Network for identifying the occurrence of metastasis in a patient with positive or negative breast cancer tumor based on observed clinical parameters. Its structure is built using K2 search algorithm with a topological order obtained from Minimum Weight Spanning Tree. Maximum Likelihood Estimation is also employed to learn parameter (prior and conditional probabilities) from network structure. For metastasis identification, the marginal probability is computed using Junction Inference Tree. Compared to Logistic Regression and Linear Discriminant Analysis, Bayesian Network gives a clearer idea about how each clinical parameter relates to another. In terms of average accuracy, sensitivity, and selectivity, Bayesian Network also outperforms those methods.

Original languageEnglish
Title of host publication2016 4th International Conference on Information and Communication Technology, ICoICT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467398794
DOIs
Publication statusPublished - 19 Sep 2016
Event4th International Conference on Information and Communication Technology, ICoICT 2016 - Bandung, Indonesia
Duration: 25 May 201627 May 2016

Publication series

Name2016 4th International Conference on Information and Communication Technology, ICoICT 2016

Conference

Conference4th International Conference on Information and Communication Technology, ICoICT 2016
Country/TerritoryIndonesia
CityBandung
Period25/05/1627/05/16

Keywords

  • Bayesian Network
  • clinical parameters
  • metastasis

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