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Apostolopoulos ID, Papandrianos NI, Papathanasiou ND, Papageorgiou EI. Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study. Bioengineering (Basel) 2024; 11:139. [PMID: 38391626 PMCID: PMC10886348 DOI: 10.3390/bioengineering11020139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.
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Affiliation(s)
| | - Nikolaos I Papandrianos
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | | | - Elpiniki I Papageorgiou
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
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Kreiner M, Viloria J. A novel artificial neural network for the diagnosis of orofacial pain and temporomandibular disorders. J Oral Rehabil 2022; 49:884-889. [PMID: 35722743 DOI: 10.1111/joor.13350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/28/2022] [Accepted: 06/02/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Temporomandibular disorders (TMD) and orofacial pain are highly prevalent. This prevalence can be compared to that of leading non-communicable diseases (NCDs). However, it is surprising to still find a high degree of controversy regarding its diagnosis and management. Patients usually experience treatment delays, missed diagnoses, and receive unnecessary therapies. New artificial intelligence algorithms have helped diagnose numerous diseases. Nevertheless, no studies have focused on the use of artificial intelligence to diagnose these conditions. OBJECTIVES This study aimed to develop and test the performance of a novel neural network (multilayer perceptron) with diagnostic capabilities in orofacial pain and TMD, including some types of referred pain. METHODS A multilayer perceptron (MLP) was developed with one input layer, five hidden layers, and one output layer. It was trained using backpropagation algorithms. Several categories of orofacial pain and TMD clinical cases were presented to 12 general dental clinicians, and their diagnoses were contrasted to those provided by the artificial intelligence neural network. RESULTS Overall, the diagnostic accuracy of the artificial intelligence was superior to that of the general dental clinicians (p = .0072). This was more evident in the clinical cases involving non-dental and referred orofacial pains (e.g. neuropathic pain, referred cardiac pain, neurovascular pain). CONCLUSIONS This study showed, for the first time, that an artificial neural network can help medical and general dental clinicians diagnose several types of orofacial pain and dysfunction, including TMD, neuropathic, neurovascular, and referred cardiac pain. In some cases, the MLP appears to have a life-saving role.
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Affiliation(s)
- Marcelo Kreiner
- Department of General and Oral Physiology, School of Dentistry, Universidad de la República, Uruguay, Montevideo, Uruguay
| | - Jesús Viloria
- Craniomandibular Function and Orofacial Pain Research Group, Universidad de la República, Uruguay, Montevideo, Uruguay
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Barbounaki SG, Sarantaki A, Gourounti K. Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics. Acta Inform Med 2021; 29:210-215. [PMID: 34759462 PMCID: PMC8563028 DOI: 10.5455/aim.2021.29.210-215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/20/2021] [Indexed: 11/27/2022] Open
Abstract
Background: Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning. Objective: The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics. Methods: A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted. Results: Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. Conclusion: This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.
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Affiliation(s)
- Stavroula G Barbounaki
- Electrical and Computer Engineer, Independent Researcher, National Merchant Marine Academy of Aspropyrgos, Aspropyrgos, Greece
| | - Antigoni Sarantaki
- Midwifery Department, Faculty of Health & Caring Sciences, University of West Attica, Athens, Greece
| | - Kleanthi Gourounti
- Electrical and Computer Engineer, Independent Researcher, National Merchant Marine Academy of Aspropyrgos, Aspropyrgos, Greece.,Midwifery Department, Faculty of Health & Caring Sciences, University of West Attica, Athens, Greece
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A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:1016284. [PMID: 33082836 PMCID: PMC7556058 DOI: 10.1155/2020/1016284] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022]
Abstract
Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.
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Nair A, Reckien D, van Maarseveen M. Generalised fuzzy cognitive maps: Considering the time dynamics between a cause and an effect. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Apostolopoulos ID, Groumpos PP. Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps. Comput Methods Biomech Biomed Engin 2020; 23:879-887. [DOI: 10.1080/10255842.2020.1768534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
| | - Peter P. Groumpos
- Electrical and Computer Engineering Department, University of Patras, Rion, Greece
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Dogu E, Albayrak YE, Tuncay E. Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Elif Dogu
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Y. Esra Albayrak
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Esin Tuncay
- Yedikule Chest Diseases & Thoracic Surgery Training & Research Hospital, Zeytinburnu, Istanbul, Turkey
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Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis. Artif Intell Med 2019; 102:101746. [PMID: 31980088 DOI: 10.1016/j.artmed.2019.101746] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 10/22/2019] [Accepted: 10/27/2019] [Indexed: 12/26/2022]
Abstract
In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.
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Dogu E, Albayrak YE. Criteria evaluation for pricing decisions in strategic marketing management using an intuitionistic cognitive map approach. Soft comput 2018. [DOI: 10.1007/s00500-018-3219-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR. A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:129-145. [PMID: 28325441 DOI: 10.1016/j.cmpb.2017.02.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 02/11/2017] [Accepted: 02/17/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures. METHODS This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment. RESULTS In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category. CONCLUSIONS Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.
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Affiliation(s)
- Abdollah Amirkhani
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Elpiniki I Papageorgiou
- Dept. of Computer Engineering, Technological Educational Institute of Central Greece, Lamia 35100, Greece.
| | - Akram Mohseni
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Mohammad R Mosavi
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2765-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Büyükavcu A, Albayrak YE, Göker N. A fuzzy information-based approach for breast cancer risk factors assessment. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.09.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Amirkhani A, Shirzadeh M, Papageorgiou EI, Mosavi MR. Visual-based quadrotor control by means of fuzzy cognitive maps. ISA TRANSACTIONS 2016; 60:128-142. [PMID: 26678850 DOI: 10.1016/j.isatra.2015.11.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 10/25/2015] [Accepted: 11/06/2015] [Indexed: 06/05/2023]
Abstract
By applying an image-based visual servoing (IBVS) method, the intelligent image-based controlling of a quadrotor type unmanned aerial vehicle (UAV) tracking a moving target is studied in this paper. A fuzzy cognitive map (FCM) is a soft computing method which is classified as a fuzzy neural system and exploits the main aspects of fuzzy logic and neural network systems; so it seems to be a suitable choice for implementing a vision-based intelligent technique. An FCM has been employed in implementing an IBVS scheme on a quadrotor UAV, so that the UAV can track a moving target on the ground. For this purpose, by properly combining the perspective image moments, some features with the desired characteristics for controlling the translational and yaw motions of a UAV have been presented. In designing a vision-based control method for a UAV quadrotor, there are some challenges, including the target mobility and not knowing the height of UAV above the target. Also, no sensor has been installed on the moving object and the changes of its yaw angle are not available. Despite all the stated challenges, the proposed method, which uses an FCM in controlling the translational motion and the yaw rotation of a UAV, adequately enables the quadrotor to follow the moving target. The simulation results for different paths show the satisfactory performance of the designed controller.
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Affiliation(s)
- Abdollah Amirkhani
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Masoud Shirzadeh
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Elpiniki I Papageorgiou
- Computer Engineering Department, Technological Educational Institute of Central Greece, Lamia, Greece.
| | - Mohammad R Mosavi
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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Can Kutlu A, Kadaifci C. Analyzing critical success factors of total quality management by using fuzzy cognitive mapping. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2014. [DOI: 10.1108/jeim-06-2012-0032] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
– Total quality management (TQM) is a process and philosophy to achieve customer satisfaction in long term by improving the products, processes and services effectively and efficiently. TQM implementation is turning into a complex practice due to the increasing number of effective factors and key elements labelled as critical success factors (CSFs). The purpose of this paper is to analyse the relations between CSFs of TQM and to provide decision makers has a clear picture of relations by determining the most affecting – both the number of CSFs which this factor affects and the its effect degree on relevant CSFs are higher comparing to other factors – of this factors affected factors – both the number of CSFs and their effect degree on these factors are higher – that influences a successful TQM implementation.
Design/methodology/approach
– The paper refers to fuzzy cognitive maps (FCMs) that allow dynamic modelling of a system in consideration of a complex network structure and the effects of factors to each other. The method demonstrates causal representations between CSFs under uncertainty to represent the relations and interaction between them and performs qualitative simulations to analyse the factors that have the highest impact on continuous improvement of quality management process. The evaluations are performed by five academicians whose professions are on both the areas of TQM and FCM.
Findings
– FCM analysis shows how the most affecting and affected factors influence the other CSF in order to manage a successful TQM implementation.
Originality/value
– The critical factors of TQM implementation are in the focus of most of the empirical studies in the literature. However, none of them considers the dynamic interactions between the factors. This study employs FCM to explore the CSFs that influence the TQM implementation process considering the relations among them to observe the most affecting and affected factors based on the changes of determined CSFs.
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Escudero-Mancebo D, González-Ferreras C, Vivaracho-Pascual C, Cardeñoso-Payo V. A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling. COMPUT SPEECH LANG 2014. [DOI: 10.1016/j.csl.2013.08.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Autonomous real-time landing site selection for Venus and Titan using Evolutionary Fuzzy Cognitive Maps. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.01.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning. Knowl Based Syst 2012. [DOI: 10.1016/j.knosys.2012.01.008] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Papageorgiou EI, Salmeron JL. Learning Fuzzy Grey Cognitive Maps using Nonlinear Hebbian-based approach. Int J Approx Reason 2012. [DOI: 10.1016/j.ijar.2011.09.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2011.01.036] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Papageorgiou EI. A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2009.12.010] [Citation(s) in RCA: 84] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Beena P, Ganguli R. Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2010.01.023] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Papageorgiou EI, Markinos AT, Gemtos TA. Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application. FUZZY COGNITIVE MAPS 2010. [DOI: 10.1007/978-3-642-03220-2_14] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Papageorgiou EI. Medical Decision Making through Fuzzy Computational Intelligent Approaches. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-04125-9_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Papageorgiou E, Spyridonos P, Glotsos DT, Stylios C, Ravazoula P, Nikiforidis G, Groumpos P. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl Soft Comput 2008. [DOI: 10.1016/j.asoc.2007.06.006] [Citation(s) in RCA: 141] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Papageorgiou E, Stylios C, Groumpos P. Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework. ACTA ACUST UNITED AC 2007; 2007:1192-5. [DOI: 10.1109/iembs.2007.4352510] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Papageorgiou E, Stylios C, Groumpos P. A combined Fuzzy Cognitive Map and decision trees model for medical decision making. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2006; 2006:6117-6120. [PMID: 17946358 DOI: 10.1109/iembs.2006.260354] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
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.
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Spyridonos P, Papageorgiou EI, Groumpos PP, Nikiforidis GN. Integration of Expert Knowledge and Image Analysis Techniques for Medical Diagnosis. LECTURE NOTES IN COMPUTER SCIENCE 2006. [DOI: 10.1007/11867661_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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