A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer&#x2019;s Disease Diagnosis

Nora Shoaip; Amira Rezk; Shaker El-Sappagh; Louai Alarabi; Sherif Barakat; Mohammed M. Elmogy

doi:10.1109/ACCESS.2020.3048435

IEEE Access (Jan 2021)

A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer’s Disease Diagnosis

Nora Shoaip,
Amira Rezk,
Shaker El-Sappagh,
Louai Alarabi,
Sherif Barakat,
Mohammed M. Elmogy

Affiliations

Nora Shoaip: ORCiD; Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
Amira Rezk: ORCiD; Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
Shaker El-Sappagh: Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
Louai Alarabi: ORCiD; Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
Sherif Barakat: Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
Mohammed M. Elmogy: ORCiD; Department of Information Technology, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

DOI: https://doi.org/10.1109/ACCESS.2020.3048435
Journal volume & issue: Vol. 9
pp. 31350 – 31372

Abstract

Read online

The World Health Organization (WHO) indicates that the proportion of the elderly will soon include nearly a quarter of the world population. Ensuring that health systems are prepared to deal with this phenomenal rate of aging and associated diseases generates many challenges. Among these challenges is facing Alzheimer's Disease (AD) that may occur at some point in the elderly life and may harm societies. AD is considered a neurological, psychological, mental, and health setback. The Clinical Decision Support System (CDSS) can improve patient care and support many medical functions, such as diagnosing diseases that can reduce preventable harm. This research's main objective is to design, implement, and evaluate the Alzheimer's Disease Diagnosis Ontology (ADDO). It is a comprehensive semantic knowledge base toward the development of fuzzy ontology-based CDSS for AD diagnosis. ADDO can serve as a core component of CDSS, which provides representation, annotation, and access to aspects related to AD's study and diagnosis. Toward the management of this objective, ADDO is based on the essentials of the Open Biomedical Ontology (OBO) and follows the Basic Formal Ontology (BFO) and the Ontology for General Medical Sciences (OGMS) principles. ADDO focuses on representing the patient characteristics, complications, drugs, diagnosis examination tests, and key aspects of their periodic visits in a standard way. The possibility of semantic interoperability is taken into account by integrating ADDO and a heterogeneous AD dataset. We used ADNI as a case study to mapping a set of real instances. To manage the medical domain's uncertainty, ADDO is extended to fuzzy ontology to accommodate the medical linguistic variables and enhance diagnosis results' efficiency. ADDO is constructed using Protégé 5.5.0 software and evaluated using the HermiT reasoner and SPARQL semantic queries. ADDO currently includes 7060 concepts, 99 properties, 46274 axioms, and 30708 annotations. As a result, ADDO is consistent and more reliable in managing most AD aspects than other existing AD ontologies.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords