الفهرس 
Chapter 1: Introductionيوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
AD is characterized as a chronic degenerative disease that involves a group of neurological disorders resulting from the accumulation of amyloid plaques that appear in the brain, affecting essential body functions. AD is a severe and complex medical problem. We tried to discuss the existing problem and the technical aspects of current AD diagnosis. CDSS can give a strong boost to the medical field. It can improve medical diagnosis by providing suitable decisions for helping physicians in selecting appropriate treatments. To build CDSS, many different techniques are used including ontology and Mamdani fuzzy inference for knowledge representation and reasoning. All of these studies did not achieve the suitable accuracy and compatibility with the electronic health record environment. Implementing a distributed CDSS system that can semantically and automatically understand the meaning of patient’s data and automatically utilize these data in its decision is a complex challenge. In this thesis, ADDO is developed as a standard fuzzy ontology-based semantic knowledge that aims to provide a warning to high-risk patients who have a high chance of having AD. A detailed analysis of patients and a timeline of patient visits is efficiently considered, including patient’s demographic data, medical history, disease history, complications, medication, and covers many diagnostic tests. ADDO supports the interoperability by adhering to BFO and OGMS top-level ontologies. Hopefully, ADDO has greater significance in the AD clinical environment. from the experimental results, ADDO provides a standard ontology and supports interoperability by integrating ADDO and heterogeneous AD data. We used ADNI to mapping a set of real instances. ADDO is evaluated by answering many SPARQL semantic queries. As an evaluation result, ADDO is consistent and reliable. Based on ontology and rule-based inference, this study established the AD knowledge base. It exploited ML and ADNI dataset to provide effective inference rules. It implemented a homogeneous reasoning system based both on semantic and relations inference. The ontologies succeeded in well expressing the concepts of a specific field and its relationships, which enhanced inquiry-based accuracy on semantic and knowledge levels. Since rules can relate properties to each other, we used the rules with the help of SWRL to enhance reasoning efficiency. SWRL can bypass the inherent limitations of expressing both ontology and rulebased. In brief, the SWRL rule-based inference based on the minimal set of biomarkers can be considered good support for clinicians to diagnose AD. Also, as part of a classification system, ADDO can be used to infer a more efficient AD diagnosis by using the ML techniques power (effectiveness in exploring the key risk disease detection patterns). This integration will help gain a deeper understanding of how the model arrives at each individual’s decision. The results show that SWRL rule reasoning can effectively improve intelligent decision-making regarding AD diagnosis. in OBFCDSS, ADDO-SWRL reasoning bases on rule-based relies on AD biomarkers only and ignores patient disease history. To handle the uncertain nature of AD biomarker data, accommodate medical linguistic variables, and solve inconsistency, this framework need to extend the ADDO rule-based to build fuzzy rule-based inference. We expect that fuzzy rule-based reasoning will make the inference system more acceptable and accurate. Besides, the entire patient’s disease history, symptoms, and drugs must be considered in the inference rules to make robust decisions. Finally, we propose OEFCDSS to support physicians in AD risk level diagnosis problem. This is a novel idea to improve the capabilities of existing fuzzy systems by integrating them with the semantic reasoning of ontologies. It explicitly defines the semantics of AD knowledge by using ontology and deal with the imprecise and vague nature of its data by using fuzzy set theory. In other words, some patient attributes are firstly designed as semantic features such as patient symptoms, drugs, and complications, and then these features and other features such as lab tests are modeled as fuzzy features. The importance of our work comes from the current lack of studies related to the integration of the formal integration between the ontology semantics and fuzzy reasoning, especially in the medical domain. The ontology acts as an integral and complementary component of the fuzzy expert system. we applied our hybrid model to develop and implement automated AD diagnosis using real ADNI cases. The resulting system is more accurate, interpretable, dynamic, and interoperable.