Constructing fuzzy ontology for cardiac arrhythmias

Fouda, Heba; Elmogy, Mohammed; Abo-Elfetoh, Ahmed; Maat, Abdel Raziq

doi:10.1109/icces.2015.7393084

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…BCFO proposed in this study is built on the disease ontology (DO) [41]representing human disease information. The DO, which semantically integrates with other standard ontologies such as MeSH, ICD, NCI's thesaurus, SNOMED and OMIM to enrich its disease and medical vocabularies, has proven useful in the biomedical community and other studies [38,58]. The motivation to build the BCFO on DO is generated from its standards and provision for consistency and sustainable descriptions of human disease terms.…”

Section: Implementation Of the Crisp And Fuzzy Ontology With Prote ´Ge Thementioning

confidence: 99%

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Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies

Oyelade

Ezugwu

Adewuyi

2021

Neural Comput & Applic

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The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

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Section: Implementation Of the Crisp And Fuzzy Ontology With Prote ´Ge Thementioning

confidence: 99%

“…The approach used for the population of the BCFO ontology with patient cases differs from the approach used in [38,40,58]. The instances of the cases and the instances of its describing features of their fuzzy ontologies were populated using either manual method or relational database.…”

Section: Instantiation Of the Fuzzy Ontologymentioning

confidence: 99%

Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies

Oyelade

Ezugwu

Adewuyi

2021

Neural Comput & Applic

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“…The class hierarchy of the fuzzy ontology is derived from biomedical and disease ontologies. On the other hand, OWL may seem to have compatibility issues and is difficult to train [20].From the literature survey, it is observed that the prevailing privacy protection approaches mainly emphasis on small-sized databases that contain a single attribute to avoid the storage and computational complexity issues. In fact, all the data attributes comprise some confidential information to be preserved.…”

Section: Related Workmentioning

confidence: 99%

Privacy Protected Medical Data Classification in precision medicine using an Ontology-based Support Vector Machine in the Diabetes Management System

2019

IJITEE

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Diabetes is a serious health issue across the globe. As stated by the International Diabetes Foundation, currently 425 million people live with diabetes globally, and another 300 million people are expecting to be at higher risk of diabetes in the year 2030.Hence, there is an urgent clinical need for an early prognosis, diagnosis, and management of diabetes and its complications. In this context, numerous intelligent machine learning and data mining approaches including Support vector machine (SVM) have been exploited for diabetes management.SVM is a prevailing supervised and discriminative data classifier which assists the healthcare and biomedical professionals to ascertain unknown data patterns by training a large volume of real-time data. Since this database is the private asset, the sensitive information should be safeguarded without compromising the utility. Even though SVM is a fast and accurate machine learning technique, it does not have the capacity to represent semantically the classification and reasoning rules which can enable more precise classification. Therefore, the objective of this research is three-fold. Firstly, in order to protect sensitive clinical data, we introduce the Kronecker product and Crow Search Algorithm (CSA) based coefficient generation technique. Secondly, we design diabetes ontology to define domain concepts and relationships and allow medical datamining. Finally, we implement the Ontology-based Support Vector Machine (Ont-SVM) classifier by assimilating privacy protection, ontology, and SVM-based classifier for the diagnosis of diabetes.The experimental results on a real-world dataset, the Pima Indian database at the UCI repository, illustrate that the proposed Ont-SVM outperforms other existing approaches in terms of privacy, accuracy, specificity, and sensitivity.

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“…Fouda et al had constructed a fuzzy ontology for cardiac arrhythmias that included symptoms, diseases, diagnosis, and treatments, utilizing Web Ontology Language (OWL2). The hierarchy and the terms of ontology were based on bio‐ontology and disease ontology.…”

Section: Motivationmentioning

confidence: 99%

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Karlekar

Gomathi

2018

Int J Communication

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Summary Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.

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Constructing fuzzy ontology for cardiac arrhythmias

Cited by 3 publications

References 9 publications

Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies

Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies

Privacy Protected Medical Data Classification in precision medicine using an Ontology-based Support Vector Machine in the Diabetes Management System

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

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