A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning

Recently, Fuzzy Grey Cognitive Map (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it is focused on solving problems with high uncertainty, under discrete incomplete and small data sets. The FGCM nodes are variables, representing grey concepts. The relationships between nodes are represented by directed edges. An edge linking two nodes models the grey causal influence of the causal variable on the effect variable. Since FGCMs are hybrid methods mixing Grey Systems and Fuzzy Cognitive Maps, each cause is measured by its grey intensity. An improved construction process of FGCMs is presented in this study, proposing an intensity value to assign the vibration of the grey causal influence, thus to handle the trust of the causal influence on the effect variable initially prescribed by experts' suggestions. The explored methodology is implemented in a well-known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection, where the FCMs have previously proved their usefulness in decision support. Through the examined medical problem, the FGCMs demonstrate their functioning and dynamic capabilities to approximate better human decision making.

[1]  Matteo Gaeta,et al.  A knowledge-based framework for emergency DSS , 2011, Knowl. Based Syst..

[2]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[3]  Seçkin Polat,et al.  A fuzzy cognitive map approach for effect-based operations: An illustrative case , 2009, Inf. Sci..

[4]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[5]  Jose L. Salmeron,et al.  Ranking fuzzy cognitive map based scenarios with TOPSIS , 2012, Expert Syst. Appl..

[6]  Chrysostomos D. Stylios,et al.  Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links , 2006, Int. J. Hum. Comput. Stud..

[7]  Elpiniki I. Papageorgiou,et al.  A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques , 2011, Appl. Soft Comput..

[8]  Michalis Glykas,et al.  Fuzzy cognitive maps in business analysis and performance-driven change , 2004, IEEE Transactions on Engineering Management.

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  F. Khan The physics of radiation therapy , 1985 .

[11]  Bart Kosko,et al.  Virtual Worlds as Fuzzy Cognitive Maps , 1994, Presence: Teleoperators & Virtual Environments.

[12]  S.X. Wu,et al.  A comparative study of some uncertain information theories , 2005, 2005 International Conference on Control and Automation.

[13]  M. Nagai,et al.  Reviewing crisp, fuzzy, grey and rough mathematical models , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[14]  GuoDong Li,et al.  A grey-based decision-making approach to the supplier selection problem , 2007, Math. Comput. Model..

[15]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[16]  Icru Prescribing, recording, and reporting photon beam therapy , 1993 .

[17]  Jose L. Salmeron,et al.  Modelling grey uncertainty with Fuzzy Grey Cognitive Maps , 2010, Expert Syst. Appl..

[18]  Panagiota Spyridonos,et al.  Advanced soft computing diagnosis method for tumour grading , 2006, Artif. Intell. Medicine.

[19]  Kun Chang Lee,et al.  Fuzzy cognitive map approach to web-mining inference amplification , 2002, Expert Syst. Appl..

[20]  Jose L. Salmeron,et al.  Benchmarking main activation functions in fuzzy cognitive maps , 2009, Expert Syst. Appl..

[21]  Rossitza Setchi,et al.  Modelling IT projects success with Fuzzy Cognitive Maps , 2007, Expert Syst. Appl..

[22]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

[23]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[24]  Jose L. Salmeron,et al.  Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps , 2012, IEEE Transactions on Software Engineering.

[25]  Jose L. Salmeron,et al.  Augmented fuzzy cognitive maps for modelling LMS critical success factors , 2009, Knowl. Based Syst..

[26]  L. Gras,et al.  Estimation of the incidence of late bladder and rectum complications after high-dose (70-78 GY) conformal radiotherapy for prostate cancer, using dose-volume histograms. , 1998, International journal of radiation oncology, biology, physics.

[27]  Jose L. Salmeron,et al.  Learning Fuzzy Grey Cognitive Maps using Nonlinear Hebbian-based approach , 2012, Int. J. Approx. Reason..

[28]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule , 2003, Australian Conference on Artificial Intelligence.

[29]  Elpiniki I. Papageorgiou,et al.  A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps , 2005, Appl. Soft Comput..

[30]  G Starkschall,et al.  Evaluation and scoring of radiotherapy treatment plans using an artificial neural network. , 1996, International journal of radiation oncology, biology, physics.

[31]  Amit Konar,et al.  Reasoning and unsupervised learning in a fuzzy cognitive map , 2005, Inf. Sci..