Structural refinement of manually created Bayesian network for prostate cancer diagnosis
by Naveen Kumar Bhimagavni; Thondepu Adilakshmi
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 6, No. 5, 2021

Abstract: In general, the structure of a Bayesian network can be learnt from the available data. In some domains like medicine, Bayesian network can be manually created by domain experts and statistical methods can be applied to refine the structure based on the data. As the data is continuously getting evolved in many real-world applications, refinement of the expert network structure is unavoidable. Existing techniques refine the manually constructed Bayesian network either by verifying the relation of a node with the remaining nodes in the network (expert Bayes) or by examining a node only with its parents (MDL principle). In this work, we propose an algorithm that verifies relation of a node only with its non-descendant nodes that are identified with Markov assumption. The proposed algorithm performs small changes to the original network and proves that a smaller number of operations are required to find the best network structure. Maximum likelihood estimation (MLE) is considered as a scoring function to calculate score for each candidate structure and selects the network with the highest score. Manually created Bayesian network has been collected for the widespread disease prostate cancer and proposed algorithm refines the network structure.

Online publication date: Mon, 07-Mar-2022

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