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false; window.loswp.work = {"work":{"id":22824118,"created_at":"2016-03-04T14:47:16.197-08:00","from_world_paper_id":149903335,"updated_at":"2024-11-15T11:46:23.853-08:00","_data":{"grobid_abstract":"Fuzzy Cognitive Maps (FCMs) are an efficient modeling method providing flexibility on the simulated system's design. They consist of nodes-concepts and weighted edges that connect the nodes and represent the cause and effect relationships among them. The performance of FCMs is dependent on the initial weight setting and architecture. This shortcoming can be alleviated and the FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This research proposes a successful attempt to combine fuzzy cognitive maps with decision tree generators. A combined Decision Tree-Fuzzy Cognitive Map (DT-FCM) model is proposed when different types of input data are available and the behavior of this model is studied. In this research work, we introduce a new hybrid modeling methodology for decision making tasks and we implement the proposed methodology at a medical problem.","publication_date":"2006,,","publication_name":"2006 International Conference of the IEEE Engineering in Medicine and Biology Society","grobid_abstract_attachment_id":"43369411"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [7629591]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":43369411,"attachmentType":"pdf"}"><img alt="First page of “A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/43369411/mini_magick20190216-4405-nraatx.png?1550321238" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="7629591" href="https://teiste.academia.edu/ElpinikiPapageorgiou"><img alt="Profile image of Elpiniki Papageorgiou" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/7629591/2997224/13189017/s65_elpiniki.papageorgiou.jpg" />Elpiniki Papageorgiou</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">4 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 22824118; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=22824118"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Fuzzy Cognitive Maps (FCMs) are an efficient modeling method providing flexibility on the simulated system's design. They consist of nodes-concepts and weighted edges that connect the nodes and represent the cause and effect relationships among them. The performance of FCMs is dependent on the initial weight setting and architecture. This shortcoming can be alleviated and the FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This research proposes a successful attempt to combine fuzzy cognitive maps with decision tree generators. A combined Decision Tree-Fuzzy Cognitive Map (DT-FCM) model is proposed when different types of input data are available and the behavior of this model is studied. In this research work, we introduce a new hybrid modeling methodology for decision making tasks and we implement the proposed methodology at a medical problem.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":43369411,"attachmentType":"pdf","workUrl":"https://www.academia.edu/22824118/A_Combined_Fuzzy_Cognitive_Map_and_Decision_Trees_Model_for_Medical_Decision_Making"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":43369411,"attachmentType":"pdf","workUrl":"https://www.academia.edu/22824118/A_Combined_Fuzzy_Cognitive_Map_and_Decision_Trees_Model_for_Medical_Decision_Making"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Conceptual decision making models with the potential of learning capabilities are more appropriate and suitable for performing such hard tasks. Decision trees are a well known technique, which has been applied in many medi- cal systems to support decisions based on a set of instances. On the other hand, the soft computing technique of Fuzzy Cogni- tive Maps (FCMs) is an effective decision making technique, which provides high performance with a conceptual represen- tation of gathered knowledge a nd existing experience. FCMs have been used for medical decision making with emphasis in radiotherapy and classification tasks for bladder tumour grad- ing. This paper proposes and presents an hybrid model de- rived from the combination and the synergistic application of the above mentioned techniques. The proposed Decision Tree- Fuzzy Cognitive Map model has enhanced operation and effec- tiveness based on both methods giving better accuracy results in medical decision tasks.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System” ","attachmentId":31824908,"attachmentType":"pdf","work_url":"https://www.academia.edu/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="31459580" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems">Fuzzy Cognitive Maps Structure for Medical Decision Support Systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fuzzy Cognitive Maps Structure for Medical Decision Support Systems","attachmentId":51816568,"attachmentType":"pdf","work_url":"https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="4394443" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4394443/_Novel_Architecture_for_supporting_medical_decision_making_of_different_data_types_based_on_Fuzzy_Cognitive_Map_Framework_">“Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework”</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework”","attachmentId":31824927,"attachmentType":"pdf","work_url":"https://www.academia.edu/4394443/_Novel_Architecture_for_supporting_medical_decision_making_of_different_data_types_based_on_Fuzzy_Cognitive_Map_Framework_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4394443/_Novel_Architecture_for_supporting_medical_decision_making_of_different_data_types_based_on_Fuzzy_Cognitive_Map_Framework_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="22836476" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22836476/A_study_on_Fuzzy_Cognitive_Map_structures_for_Medical_Decision_Support_Systems">A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31758301" href="https://teiep.academia.edu/CStylios">Chrysostomos Stylios</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, 2013</p><p class="ds-related-work--abstract ds2-5-body-sm">This study examines and compares different Fuzzy Cognitive Map structures that researchers have proposed for developing Medical Decision Support Systems. Fuzzy Cognitive Maps are a soft computing technique that have gained a good reputation in the last decade and have been used successfully in different medical fields for decision making, diagnosis and classification.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems","attachmentId":43381544,"attachmentType":"pdf","work_url":"https://www.academia.edu/22836476/A_study_on_Fuzzy_Cognitive_Map_structures_for_Medical_Decision_Support_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22836476/A_study_on_Fuzzy_Cognitive_Map_structures_for_Medical_Decision_Support_Systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="4195074" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4195074/Fuzzy_cognitive_map_architectures_for_medical_decision_support_systems"> Fuzzy cognitive map architectures for medical decision support systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":" Fuzzy cognitive map architectures for medical decision support systems","attachmentId":31691315,"attachmentType":"pdf","work_url":"https://www.academia.edu/4195074/Fuzzy_cognitive_map_architectures_for_medical_decision_support_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4195074/Fuzzy_cognitive_map_architectures_for_medical_decision_support_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="22824156" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22824156/A_Novel_Approach_on_Designing_Augmented_Fuzzy_Cognitive_Maps_Using_Fuzzified_Decision_Trees">A Novel Approach on Designing Augmented Fuzzy Cognitive Maps Using Fuzzified Decision Trees</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7629591" href="https://teiste.academia.edu/ElpinikiPapageorgiou">Elpiniki Papageorgiou</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Communications in Computer and Information Science, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from both symbolic and numeric data. Fuzzy theoretical techniques are used to fuzzify crisp decision trees in order to soften decision boundaries at decision nodes inherent in this type of trees. Comparisons between crisp decision trees and the fuzzified decision trees suggest that the later fuzzy tree is significantly more robust and produces a more balanced decision making. The approach proposed in this paper could incorporate any type of fuzzy decision trees. Through this methodology, new linguistic weights were determined in FCM model, thus producing augmented FCM tool. The framework is consisted of a new fuzzy algorithm to generate linguistic weights that describe the cause-effect relationships among the concepts of the FCM model, from induced fuzzy decision trees.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Novel Approach on Designing Augmented Fuzzy Cognitive Maps Using Fuzzified Decision Trees","attachmentId":43369462,"attachmentType":"pdf","work_url":"https://www.academia.edu/22824156/A_Novel_Approach_on_Designing_Augmented_Fuzzy_Cognitive_Maps_Using_Fuzzified_Decision_Trees","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22824156/A_Novel_Approach_on_Designing_Augmented_Fuzzy_Cognitive_Maps_Using_Fuzzified_Decision_Trees"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="13340562" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13340562/Case_Based_Fuzzy_Cognitive_Maps_CBFCM_New_method_for_medical_reasoning_Comparison_study_between_CBFCM_FCM">Case Based Fuzzy Cognitive Maps (CBFCM): New method for medical reasoning: Comparison study between CBFCM/FCM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32580297" href="https://obs-vlfr.academia.edu/DrDoualiNassim">Nassim Douali</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Doctor usually uses his experience from the clinical practice to confirm a diagnosis and to prescribe an appropriate treatment for a specific patient. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such knowledge and experience are vague and define uncertain relationships between facts and diagnosis. Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps (FCM) that allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve FCM decision support systems. Semantic web is used to implement both FCM approaches. A database of 71 patients with urinary tract infections was used to perform the proposed approach. A comparative study between FCM (92%) and CBFCM (99%) was conducted and the results derived by CBFCM approach showed CBFCM to be superior to FCM.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Case Based Fuzzy Cognitive Maps (CBFCM): New method for medical reasoning: Comparison study between CBFCM/FCM","attachmentId":45449077,"attachmentType":"pdf","work_url":"https://www.academia.edu/13340562/Case_Based_Fuzzy_Cognitive_Maps_CBFCM_New_method_for_medical_reasoning_Comparison_study_between_CBFCM_FCM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13340562/Case_Based_Fuzzy_Cognitive_Maps_CBFCM_New_method_for_medical_reasoning_Comparison_study_between_CBFCM_FCM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="13340590" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13340590/Clinical_Decision_Support_System_based_on_Fuzzy_Cognitive_Maps">Clinical Decision Support System based on Fuzzy Cognitive Maps</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32580297" href="https://obs-vlfr.academia.edu/DrDoualiNassim">Nassim Douali</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Computer Science & Systems Biology, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">Citation: Douali N, Papageorgiou EI, Roo JD, Cools H, Jaulent MC (2015) Clinical Decision Support System based on Fuzzy Cognitive Maps. J Comput Sci Syst Biol 8: 112-120.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Clinical Decision Support System based on Fuzzy Cognitive Maps","attachmentId":38018771,"attachmentType":"pdf","work_url":"https://www.academia.edu/13340590/Clinical_Decision_Support_System_based_on_Fuzzy_Cognitive_Maps","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13340590/Clinical_Decision_Support_System_based_on_Fuzzy_Cognitive_Maps"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="4195080" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions">Complementary case-based reasoning and competitive Fuzzy cognitive maps for advanced medical decisions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Complementary case-based reasoning and competitive Fuzzy cognitive maps for advanced medical decisions","attachmentId":31691318,"attachmentType":"pdf","work_url":"https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="22824071" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22824071/A_Generic_Framework_Combining_Different_Data_Types_for_Fuzzy_Cognitive_Map_Based_Decision_Support">A Generic Framework Combining Different Data Types for Fuzzy Cognitive Map Based Decision Support</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7629591" href="https://teiste.academia.edu/ElpinikiPapageorgiou">Elpiniki Papageorgiou</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In medical problems, we often have to deal with different types of variables and data. The data can be, for example, symbolic or numeric and data analysis methods can often deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The frameworks for medical decision support are mostly based on one type of available data and the neuro-fuzzy learning approaches have been proposed to process symbolic and ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Generic Framework Combining Different Data Types for Fuzzy Cognitive Map Based Decision Support","attachmentId":43369365,"attachmentType":"pdf","work_url":"https://www.academia.edu/22824071/A_Generic_Framework_Combining_Different_Data_Types_for_Fuzzy_Cognitive_Map_Based_Decision_Support","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22824071/A_Generic_Framework_Combining_Different_Data_Types_for_Fuzzy_Cognitive_Map_Based_Decision_Support"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":43369411,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":43369411,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_43369411" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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href="https://www.academia.edu/82732513/A_review_of_fuzzy_cognitive_maps_in_medicine_Taxonomy_methods_and_applications">A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="8602948" href="https://independent.academia.edu/akrammohseni">akram mohseni</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computer Methods and Programs in Biomedicine, 2017</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and 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ds2-5-body-link" href="https://www.academia.edu/4392344/_Genetic_Algorithm_enhanced_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_">“Genetic Algorithm enhanced Fuzzy Cognitive Maps for Medical Diagnosis”</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Genetic Algorithm enhanced Fuzzy Cognitive Maps for Medical Diagnosis”","attachmentId":31823600,"attachmentType":"pdf","work_url":"https://www.academia.edu/4392344/_Genetic_Algorithm_enhanced_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span 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