© 2019 Ahmed Sameh. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license. Journal of Computer Science Original Research Paper Knowledge Based Bayesian Network Construction Algorithm for Medical Data Fusion to Enhance Services and Diagnosis Ahmed Sameh Department of Computer and Information Systems, Prince Sultan University, Riyadh 11586, Saudi Arabia Article history Received: 7-05-2018 Revised: 14-12-2018 Accepted: 27-05-2019 Email: asameh@psu.edu.sa Abstract: Traditional Bayesian networks' algorithms are treating the network construction process as an isolated and autonomous data-driven trial-and-error process and completely ignoring the domain knowledge. In this work we are proposing a new 'Semantically Aware Ontology-Based Bayesian Network construction algorithm' that is knowledge centered instead of data centered. The objective of the new algorithm is to empower patients through improving their self-diagnosis and testing by automatically constructing a set of Ontology- Based Bayesian networks using combination of domain and expert knowledge. The exciting thing about the proposed algorithm is that it uses on 'Saudi-native training data' streamed from the “Unified Medical Record” server and authenticated domain and expert knowledge extracted from the “King Abdulla Encyclopedia” server. A proof-of-concept prototype based on open-source software “Netica” and “Protégé” is implemented and tested. It demonstrates learning of probabilities, network structure and mixes discrete and continuous variables. It imports “Diabetes” patient medical record steams from the “Unified Medical Record” server to be used as training and testing datasets. It also extracts Bayesian data variables from the “King Abdullah Encyclopedia” server to aid in constructing and learning the ontology-based Bayesian networks. The prototype is implemented on an Internet server and can be accessed from medical applications on Smartphones and PDAs. It currently deals with 60 positive “Diabetes” Saudi patients and 60 negative "Diabetes" training cases. The resulting Ontology Bayesian network was tested on another 100 test cases drawn randomly from the 'Unified Medical records' server. An accuracy of diagnosis of 100% was achieved on the test data. Keywords: Home Healthcare, Bayesian Network, Ontology, Medical Encyclopedia, Unified Medical Records, Self-Diagnosis Introduction A paradigm shift from traditional data mining to semantic data mining is leading to Knowledge Centered instead of Data Centered data mining techniques. Integrating Ontology and Bayesian Network is currently promoting the enrichment of our medical domain knowledge. At the moment, open interoperable ontologies are enabling medical stakeholders to communicate with no ambiguity. Unfortunately obtaining action items from such ontologies is done through deterministic reasoning which are unsuitable for the medical field. Besides, Bayesian networks offer an intuitive representation of uncertainty, which can be integrated with medical ontologies through few steps. This integration can be done by extracting the Bayesian variables (noun nodes) that are core in the specific medical domain along with their possible attributes/values from the ontologies, mapping the relationships (verb nodes) between these variables through consulting the appropriate ontologies and then calculating the conditional probabilities required for the Bayesian nodes. It is well known that medicine and healthcare generally lag behind in technology adoption. Thus, Kingdom of Saudi Arabia is trying to enhance its medical and healthcare sector through a number of national technology-based mega projects. Two notable projects are the Ministry of Health’s “Unified Medical Record” project (MHUMR, 2016) and the National Guard’s “King Abdullah Encyclopedia of Arabic Health Content” project (2018). The first project was launched by KSA’s Ministry of Health in 2008 and incremental implementation has already been finished in the