Research Article Open Access Appiah et al J Health Med Informat 2019, 10:4 Volume 10 • Issue 4 • 1000338 J Health Med Inform, an open access journal ISSN: 2157-7420 Journal of Health & Medical Informatics J o u r n a l o f H e a lth & M e d i c a l I n f o r m a t i c s ISSN: 2157-7420 Keywords: Ontological engineering; Knowledgebase; Ontology; Prevalent diseases Introduction Prevalent diseases are diseases, which are widespread in a particular area at a particular period. According to Russell in 2004, the outbreak of prevalent diseases imposes a high economic burden on patients, their families and the society as well as the loss of workforce and its accompanying emotional stress [1,2]. Hence, it is signifcant to set up adequate awareness creation, preventive strategies, response management and curable care to manage the prevalent diseases. Te climate condition of the Sunyani Municipality has been reported to be a signifcant contributing parameter which triggers the prevalence of diseases such as malaria, meningococcal meningitis, yellow fever and measles by Financing. Te capacity of health centres in the municipality is ofen over-stretched when such cases arise. Health centres in the municipality, however, do not have a central knowledge base of these diseases. In this paper, we provided a detailed and sound description of prevalent diseases and their relations/interactions relative to the eliminating factors in Sunyani Municipality. Specifcally, we surveyed to identify the prevalent diseases in the municipality. Consequently, we designed an ontological knowledge base of prevalent diseases in Sunyani Municipality. Succinctly, it is hoped that the study will aid health care professionals and decision-makers to efectively design to develop and deploy their strategic plans in managing the outbreak of these diseases. Literature Review Tere have been intense research eforts on diferent ontological methods to design and implement shareable and interoperable knowledge bases for public health care system to facilitate efective health care systems. However, the signifcant challenges are semantic and syntactic heterogeneity in health data Sunitha and Golla in 2014 [3]. Due to the heterogeneity nature of health data, Schulz and Martínez-costa in 2013 stated that Semantic in- teroperability of clinical information remains a largely unresolved issue [4]. A research conducted by Kuziemsky and Lau in 2010 illustrated four stages of Abstract Several works in healthcare diseases support systems in recent time are being inspired by a lot of semantic web technology. Specifcally, there has been a rise in the number of knowledgebase system that has been developed using ontological engineering. For two decades, Sunyani Municipality records a lump number of diseases with a few such as Typhoid Fever, Malaria, Diarrhoea Diseases, Pneumonia, Anaemia, and so on being prevalent. Healthcare systems in the Municipal do not have a centralised knowledge base for these prevalent diseases, hence the need for a centralised knowledge-based system. This study proposes a knowledge-based system using ontological engineering to assist the formulation of a strong foundation for establishing a meaningful decision-making support system for the proper diagnosis and management of these diseases in the Municipality. We analysis 3,377,403 number of cases from 2013- 2017 and thereafter categorised the case into different classes of diseases. Using a threshold ratio of +1% between several cases for a particular disease (Pdc) and total number cases in its category (Cr), we characterised about thirty- fve (35) diseases as prevalent. Consequently, we designed a robust knowledge-based for the identifed prevalent diseases by adopting the Cyc method, which includes three processes in connection with ontological engineering technique. The system was well rated of about 77% after staff from two primary health facilities in the municipality. A Knowledge-Base of Prevalent Diseases in Sunyani Municipality, Ghana Using Ontological Engineering Stephen Appiah*, Adebayo Felix Adekoya, Crispin Bapuuroh and Christian Akowua-Kwakye Department of Computer Science and Informatics, University of Energy and Natural Resources, P. O. Box 214, Sunyani, Ghana *Corresponding author: Stephen Appiah, Department of Computer Science and Informatics, University of Energy and Natural Resources, P. O. Box 214, Sunyani, Ghana, Tel: 233242279931; E-mail: stephen.appiah.stu@uenr.edu.gh Received October 10, 2019; Accepted October 30, 2019; Published November 07, 2019 Citation: Appiah S, Adekoya AF, Bapuuroh C, Akowua-Kwakye C (2019) A Knowledge-Base of Prevalent Diseases in Sunyani Municipality, Ethiopia Using Ontological Engineering. J Health Med Informat 10: 338. Copyright: © 2019 Appiah S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ontology-based health information system. Te states were presented as specifcation and conceptualisation, formalisation, implementation and evaluation, and maintenance [5]. Research conducted by Sunitha and Golla in 2014 in some countries revealed that medical and health care ontologies are widely used to; improve the accuracy of diagnoses by providing real-time correlations of symptoms, test results and individual medical histories, help build more robust and diferent healthcare information systems, assist the need of process healthcare patient data in transmission and reuse, and draws meaning from healthcare data [3]. Zeshan and Mohamad in 2012 developed a medical ontology for handling road accident injuries by providing immediate and quick knowledge supply. Ontology enhances sharing and reusing of knowledge supported by reasoning tools for the extraction of new knowledge are realised [6]. Ontology knowledge base, as described by Jasper and Uschold in 1999, and Lambrix and Tan, ofers communication between human, among sofware, and interoperability [7,8]. Ontology knowledge has an essential impact on the healthcare domain, as stated by Raghupahia and Tan in 2012 in the United States spent 21.6 billion Dollars in healthcare-related technologies [9]. Knowledgebase Formulation A complete knowledge base of prevalent diseases helps the policymakers and health professionals to derive conclusions and take right decisions. Te knowledge base becomes benefcial decision- making source. Ontology-based semantic techniques provide excellent