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https://www.researchgate.net/publication/224141217_A_Fuzzy_Expert_System_for_Diabetes_Decision_Support_Application
Chang S. L. and Mei H. W. (2011) [44] "A fuzzy expert system for diabetes decision support application" Fuzzy expert system Diabetes decision support The suggested fuzzy expert system can perform ...
https://www.ncbi.nlm.nih.gov/pubmed/20501347
It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for …Cited by: 224
http://www.elearning.upnjatim.ac.id/courses/KECERDASANBUATAN/work/50afa90656fffA_Fuzzy_Expert_System_for_Diabetes_Decision_Support_Application.pdf
A Fuzzy Expert System for Diabetes Decision Support Application Chang-Shing Lee, Senior Member, IEEE, and Mei-Hui Wang Abstract—An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It …
https://www.longdom.org/open-access/a-decision-support-system-for-diabetes-mellitus-management-.pdf
It can be difficult for an expert to transfer their knowledge into distinct ... Elmogy M (2016) A Decision Support System for Diabetes Mellitus Management. Diabetes Case Rep 1:102. doi: 10.4172/2572-5629.1000102 Page 2 of 13 oe 1 e 1 10000102 ... For diabetes, the existing fuzzyCited by: 8
https://www.researchgate.net/publication/303803655_A_Decision_Support_System_for_Diabetes_Mellitus_Management
Diabetes mellitus is considered as a dangerous chronic disease. Diagnosis is the first step in its management. Clinical decision support system (CDSS) for diabetes diagnosis improves its detection ...
http://www.ijana.in/papers/V3I2-12.pdf
fuzzy expert system for diabetes decision support application based on the fuzzy ontology with five layer fuzzy ontology. Ismail saritas et al.[9] developed a fuzzy expert system to determine drug dose in treatment of chronic interstine inflamation using the concept of fuzzification. Mehdi Fasanghari et al.[10] developed a fuzzy expert system for
http://cstl-hcb.semo.edu/eom/iebmdssrwweb.PDF
2 Architecture of decision support systems 3 Decision support system sub-specialities 4 Sub-specialities based on organizational perspectives 5 Application development research 6 The future of decision support systems Overview Decision support systems (DSS) are a subset of computer -based information systems (CBIS). The general term
https://www.ijcaonline.org/archives/volume182/number3/mujawar-2018-ijca-917482.pdf
expert system are merging of designing of expert system and web application together. Such developments can be considered as web engineering applications [4]. This research work proposes online expert system application (Web-FESSRADM) which uses fuzzy logic for drawing diabetes risk assessment. Proposed Fuzzy Expert System
https://www.ripublication.com/ijaerspl2019/ijaerv14n4spl_16.pdf
insulin, it leads to Type 1, Type 2 Diabetes. Fuzzy Expert System are extensively used in both Applied and Experimental Medicine and are one of the most prevalent subjects of Today’s Medical Informatics. All designed Fuzzy Expert System can help in support decision process of physicians. This paper
https://research.ijcaonline.org/volume63/number11/pxc3885466.pdf
application of fuzzy expert system and decision tree for selection of remedy in homoeopathy is among the rare application.76 The other areas of applications of fuzzy logic are: prediction of aneurysm, fracture healing 77,78 and in non-stationary FES, intuitionistic fuzzy …
https://www.researchgate.net/publication/224141217_A_Fuzzy_Expert_System_for_Diabetes_Decision_Support_Application
Request PDF A Fuzzy Expert System for Diabetes Decision Support Application An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical ...
https://www.ncbi.nlm.nih.gov/pubmed/20501347
Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.Cited by: 224
https://www.longdom.org/open-access/a-decision-support-system-for-diabetes-mellitus-management-.pdf
With respect to diabetes, it has utilized fuzzy ontologies in many domains such as [15]. Lee and Wang [15] proposed a five-layer fuzzy ontology and utilized it in a fuzzy expert system for diabetes management. As stated before, CBR is the most suitable mechanism for managing ill-formed problems as diabetes diagnosis.Cited by: 8
https://ieeexplore.ieee.org/document/5471158/citations
May 24, 2010 · A Fuzzy Expert System for Diabetes Decision Support Application Abstract: An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease.Cited by: 224
https://www.ripublication.com/ijaerspl2019/ijaerv14n4spl_16.pdf
insulin, it leads to Type 1, Type 2 Diabetes. Fuzzy Expert System are extensively used in both Applied and Experimental Medicine and are one of the most prevalent subjects of Today’s Medical Informatics. All designed Fuzzy Expert System can help in support decision process of physicians. This paper
http://www.elearning.upnjatim.ac.id/courses/KECERDASANBUATAN/work/50afa90656fffA_Fuzzy_Expert_System_for_Diabetes_Decision_Support_Application.pdf
tic fuzzy decision making mechanism. The proposed fuzzy expert system can give a semantic description for diabetes and support for the justification of the medical staff. Experimental results indicate that the proposed fuzzy expert system can work more effectively than other methods can [4], [5], [8]. The remainder of this paper is organized as follows.
https://www.researchgate.net/publication/313421711_Diagnosis_of_kidney_disease_using_Fuzzy_expert_system
We present a fuzzy expert system for intelligent support of decision making about cause of stone construction crack of building. The system is based on some linguistic expert expressions ...
http://www.ijana.in/papers/V3I2-12.pdf
Kahramanli and Allahverdi [7] designed a hybrid neural network system for classification of the diabetes database. Chang-Shing Lee [8] designed as fuzzy expert system for diabetes decision support application based on the fuzzy ontology with five layer fuzzy ontology.
https://www.ijcaonline.org/archives/volume182/number3/mujawar-2018-ijca-917482.pdf
6 decision support system using fuzzy logic designed in [5] for disease diagnosis where kidney stone and kidney infection were taken as case studies. Proposed work considers uncertainty of user inputs. Symptoms of kidney stone and kidney infection are used with linguistic and numeric weights.
https://pdfs.semanticscholar.org/eafd/ea06efc9160fadfdda90300dc029871a51e4.pdf
6 APPLICATIONS OF FUZZY LOGIC IN MEDICAL FIELD 6.1 Tuberculosis A fuzzy rule based system is designed to serve as a decision support for tuberculosis diagnosis. This system is designed to detect class of tuberculosis and these fuzzy rules are updated using rule mining techniques. Based on this method that
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