Open Access

ARTICLE

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

Nora Shoaip¹, Amira Rezk¹, Shaker EL-Sappagh^2,3, Tamer Abuhmed^4,*, Sherif Barakat¹, Mohammed Elmogy⁵

1 Department of Information Systems, Faculty of Computers and Information, Mansoura University, Egypt
2 Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes (CiTIUS) Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain
3 Department of Information Systems, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt
4 Department of Computer Science and Engineering, College of Computing, Sungkyunkwan University, Republic of Korea
5 Department of Information Technology, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

* Corresponding Author: Tamer Abuhmed. Email:

Computers, Materials & Continua 2021, 69(3), 3531-3548. https://doi.org/10.32604/cmc.2021.019069

Received 31 March 2021; Accepted 01 May 2021; Issue published 24 August 2021

Abstract

Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction. In this regard, the need becomes more urgent for biomarker-based detection. A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers, such as genetics, magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), and cognitive scores. Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful. Thus, our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology (ADDO) and the expression of semantic web rule language (SWRL). This work implements an ontology-based application that exploits three different machine learning models. These models are random forest (RF), JRip, and J48, which have been used along with the voting ensemble. ADNI dataset was used for this study. The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1% and precision of 94.3%. Our approach provides effective inference rules. Besides, it contributes to a real, accurate, and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive (NC), significant memory concern (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), or AD.

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