Integration of Healthcare Domain Ontologies using Bayesian Networks
Monika P1, G T Raju2

1Monika P, Research Scholar, R&D Centre, Assistant Professor, Department of CSE, RNS Institute of Technology, Dayananda Sagar College of Engineering, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India. 

2G T Raju, Professor, Department of CSE, RNS Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India. 

Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 46-52 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10281292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1028.1292S19

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Abstract: Semantic Web (SW) was created with the vision of knowledge sharing. Knowledge from the past and present help predict the future with the use of Machine Learning (ML) algorithms. SW powered with ontologies help in realizing machine interactions supporting automated knowledge extraction. Healthcare as a field of medical domain gives lot of importance for timely accurate decisions with the available features. Representing existing information in terms of ontologies, retrieving the decisions upon establishing interaction between the relevant ontologies within the same domain, knowledge sharing & reusing the existing facts are of great benefit to the medical practitioners and researchers which has lot of open challenges to be resolved in order to realize the same. To address the stated issues, an algorithmic approach – Ontologies Integration algorithm using Bayesian Networks (OIBN) based on Bayesian Belief Networks (BBN) working on Naïve beliefs has been proposed which works on symptoms through the attributes of related ontologies within the same domain exploring the symptom dependencies and their probability of occurrences in combination. Selection of features for integration will follow the steps proposed in Sequential Forward Feature Selection algorithm (SFFS). The observation on the correctness of the presented method over diabetic datasets represented in ontological form with integration of relevant features reveals that the knowledge graphs have been efficiently explored discovering the facts based on the probability theory. The experimental results conclude that the proposed technique is showing enhanced prediction accuracy of 80.95% which is better compared to accuracies of the individual ontologies prior to integration and existing state-of-art technique.

Keywords: Semantic Web, Ontologies, Ontology Agents, Ontologies Integration, Health Care, Diabetology, Domains.
Scope of the Article: Healthcare Informatics