A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method

Computer Methods and Programs in Biomedicine

Volumn 122, Issue 2, 2015, Pages 123-135

  Papageorgiou, Elpiniki I ^a   Subramanian, Jayashree ^b   Karmegam, Akila ^c   Papandrianos, Nikolaos ^d  

^a University of Central Greece   (Greece)

^b RVS COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

^c KUMARAGURU COLLEGE OF TECHNOLOGY   (India)

^d Diagnostic Medical Center Diagnostiki AE   (Greece)

Author keywords

Familial breast cancer; Fuzzy Cognitive Maps; Hebbian learning; Medical decision support systems; Risk assessment

Indexed keywords

ARTIFICIAL INTELLIGENCE; COGNITIVE SYSTEMS; DECISION SUPPORT SYSTEMS; DIAGNOSIS; DISEASES; FUZZY RULES; FUZZY SYSTEMS; HEALTH CARE; HEALTH RISKS; LARGE SCALE SYSTEMS; LEARNING SYSTEMS; RISK ASSESSMENT; SOFT COMPUTING;

10-FOLD CROSS-VALIDATION; BREAST CANCER; FUZZY COGNITIVE MAP; GENETIC PREDISPOSITION; HEALTH CARE PROFESSIONALS; HEBBIAN LEARNING; MEDICAL DECISION SUPPORT SYSTEM; RISK MANAGEMENT SYSTEMS;

RISK MANAGEMENT;

ARTICLE; BREAST CANCER; CANCER PATIENT; CLINICAL ARTICLE; CLINICAL EVALUATION; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FAMILIAL BREAST CANCER; FAMILIAL CANCER; FEMALE; FUZZY COGNITIVE MAP; GAIL MODEL; HEALTH CARE PERSONNEL; HUMAN; INDIVIDUALIZATION; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL MODEL; MEDICAL RECORD; NSAPB MODEL; PATIENT CARE; PREDICTIVE VALUE; RISK ASSESSMENT; RISK FACTOR; RISK MANAGEMENT; STATISTICAL MODEL; TYRER CUZICK MODEL; VALIDATION STUDY; ADULT; AGED; BREAST NEOPLASMS; CLINICAL DECISION SUPPORT SYSTEM; COMPUTER ASSISTED DIAGNOSIS; FUZZY LOGIC; GENETIC PREDISPOSITION; GENETICS; MIDDLE AGED; ORGANIZATION AND MANAGEMENT; PERSONALIZED MEDICINE; PREVALENCE; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ADULT; AGED; BREAST NEOPLASMS; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; FUZZY LOGIC; GENETIC PREDISPOSITION TO DISEASE; HUMANS; MACHINE LEARNING; MIDDLE AGED; PRECISION MEDICINE; PREVALENCE; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY;

EID: 84944281066     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.07.003     Document Type: Article

References (56)

1. Jemal A., et al. Global cancer statistics. CA: Cancer J. Clin. 2011, 61(2):69-90.
2. Breast cancer basics, . http://www.webmd.com/breast-cancer/guide/understanding-breast-cancer-basics.
3. Nice guideline, . https://www.nice.org.uk/guidance/conditions-and(/cancer/breast-cancer.
4. Breast cancer risk assessment tool, . http://www.cancer.gov/bcrisktool/.
5. Amir E., Freedman O. Underestimation of risk by Gail model extends beyond women with atypical hyperplasia. J. Clin. Oncol. 2009, 27(9):1526.
6. Beenken S.W., Bland K.L. Chapter 5: breast cancergenetics. Inherited Cancer Syndrome 2004, Springer - verlag Inc., New York. C. Neal Ellis (Ed.).
7. Gail M.H., Mai P.L. Comparing breast cancer risk assessment models. J. Natl. Cancer Inst. 2010, 102(10):665-668.
8. Challa V.R., Swamyvelu K., Shetty N. Assessment of the clinical utility of the Gail model in estimating the risk of breast cancer in women from the Indian population. Ecancermedicalscience 2013, 7(October):363. eCollection 2013. 10.3332/ecancer.2013.363.
9. Capalbo C., Ricevuto E., Vestri A., Ristori E., Sidoni T., Buffone O., Adamo B., et al. BRCA1 and BRCA2 genetic testing in Italian breast and/or ovarian cancer families: mutation spectrum and prevalence and analysis of mutation prediction models. Ann. Oncol. 2006, 17(Suppl. 7):vii34-vii40.
10. Risk evaluator Bellcross C: approaches to applying breast cancer risk prediction models in clinical practice. Oncol. Genet. 2009, http://www.ems-trials.org/riskevaluator/.
11. National Surgical Adjuvant Breast Project (NSABP), . http://halls.md/gail-model-risk/.
12. Amir E., Evans D.G., Shenton A., Lalloo F., Moran A., Boggis C., Wilson M., Howell A. Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J. Med. Genet. 2003, 40(11):807-814.
13. Claus E.B., Risch N., Thompson W.D. The calculation of breast cancer risk for women with a first degree family history of ovarian cancer. Breast Cancer Res. Treat. 1993, 28(2):115-120.
14. Fuzzy Cognitive Maps for Applied Sciences and Engineering - From Fundamentals to Extensions and Learning Algorithms 2014, 3973. Intelligent Systems Reference Library 54, Springer. E.I. Papageorgiou (Ed.).
15. Papageorgiou E.I. Review study on fuzzy cognitive maps and their applications during the last decade. IEEE International Conference on Fuzzy Systems 2011.
16. Froelich W., Papageorgiou E.I., Samarinas M., Skriapas K. Application of evolutionary FCMs to the long-term prediction of prostate cancer. Appl. Soft Comput. J. 2012, vol. 12(December (12)):3810-3817.
17. Beena P., Ranjan, Ganguli Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 2011, 11(1):1014-1020.
18. Subramanian J., Karmegam A., Papageorgiou E., Papandrianos N., Vasukie A. An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. Comput. Methods Prog. Biomed. 2015, 118(March (3)):280-297.
19. Papageorgiou E.I., Huszka C., De Roo J., Douali N., Jaulent M.C., Colaert D. Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support. Comput. Methods Prog. Biomed. 2013, 112(December (3)):580-598.
20. Giabbanelli P.J., Crutzen R. Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach. BMC Med. Res. Methodol. 2014, 14(December):130. 10.1186/1471-2288-14-130.
21. Mpillis A., Papageorgiou E.I., Frantzidis C.A., Tsatali M.S., Tsolaki A.C., Bamidis P.D. A decision-support framework for promoting independent living and ageing well. IEEE J. Biomed. Health Inform. 2015, 19(1):199-209. 10.1109/JBHI.2014.2336757.
22. Papageorgiou E.I., Spyridonos P.P., Glotsos D.T., Stylios C.D., Ravazoula P., Nikiforidis G.N., Groumpos P.P. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl. Soft Comput. 2008, 8(January (1)):820-828.
23. Kannappan A., et al. Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Syst. Appl. 2011, 38:1282-1292.
24. Papageorgiou E.I., Kannappan A. Fuzzy cognitive map ensemble learning paradigm to solve classification problems: application to autism identification. Appl. Soft Comput. 2012, 12(12):3798-3809.
25. Mago, et al. Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping. BMC Med. Inform. Decis. Making 2012, 12:98.
26. Lee S., et al. Development of a decision making system for selection of dental implant abutments based on the fuzzy cognitive map. Expert Syst. Appl. 2012, 11564-11575.
27. Papageorgiou E.I., Papandrianos N.I., Karagianni G., Sfyras D. Fuzzy Cognitive Map based approach for assessing pulmonary infections. Lect. Notes Comput. Sci./LNAI 2009, 5722:109-118.
28. Rauch J., et al. Proceedings of 18th International Symposium on Methodologies for Intelligent Systems, ISMIS 2009 2009, Springer-Verlag Berlin Heidelberg.
29. Papageorgiou E.I. Fuzzy Cognitive Map software tool for treatment management of uncomplicated Urinary Tract Infection. Comput. Methods Prog. Biomed. J. 2012, 105(March (3)):233-245.
30. Lopes M.H., Ortega N.R., Silveira P.S., Massad E., Higa R., Marin Hde F. Fuzzy cognitive map in differential diagnosis of alterations in urinary elimination: a nursing approach. Int. J. Med. Inform. 2013, 82(March (3)):201-208. 10.1016/j.ijmedinf.2012.05.012.
31. Papakostas G.A., Koulouriotis D.E., Polydoros A.S., Tourassis V.D. Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems. Expert Syst. Appl. 2012, 39(12):10620-10629.
32. Amirkhani A., Mosavi M.R., Mohammadizadeh F., Shokouhi S.B. Classification of intraductal breast lesions based on the fuzzy cognitive map. Arab. J. Sci. Eng. 2014, 39:3723-3732.
33. Papageorgiou E.I., Salmeron J.L. Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach. Int. J. Approx. Reason. 2012, 53.(1):54-65.
34. Stach W., Kurgan L., Pedrycz W. Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. FUZZ-IEEE 2008 (IEEEWorld Congress on Computational Intelligence). IEEE International Conference on Fuzzy Systems 2008.
35. Papageorgiou E.K.E., Parsopoulos C.D., Stylios P.P., Groumpos M.N.V. Fuzzy cognitive maps learning using particle swarm optimization. Int. J. Intell. Inf. Syst. 2005, 25(1):95-121.
36. Chen Y., Mazlack L., Lu L. Learning fuzzy cognitive maps from data by ant colony optimization. Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference 2012.
37. Evans D.G., Howell A. Breast cancer risk-assessment models. Breast Cancer Res. 2007, 9(5):213.
38. Armstrong K., Eisen A., Weber B. Assessing the risk of breast cancer. N. Engl. J. Med. 2000, 342(8):564-571.
39. Siegel R., et al. Cancer statistics, 2011. CA: Cancer J. Clin. 2011, 61(4):212-236.
40. Cancer Data and Statistics, . http://www.cdc.gov/features/datastatistics.html.
41. http://www.cdc.gov/genomics/resources/diseases/breast_ovarian_cancer/risk_categories.htm#relatives.
42. Breast cancer guidelines, . http://www.nccn.org/professionals/physician_gls/f_guidelines.asp#breast_risk.
43. Breast Cancer Risk Factors, . http://www.breastcancer.org/risk/factors/.
44. Causes, risk factors, and prevention topics, . http://www.cancer.org/cancer/breastcancer/detailedguide/.
45. Breast cancer risk factors, . http://www.nationalbreastcancer.org/breast-cancer-risk-factors.
46. http://ww5.komen.org/BreastCancer/FamilyHistoryofBreastOvarianorProstateCancer.html#sthash.dEi3Otz0.dpuf.
47. Collaborative Group on Hormonal Factors in Breast Cancer Menarche, menopause and breast cancer risk: individual participant meta-analysis, including 118,964 women with breast cancer from 117 epidemiological studies. Lancet Oncol. 2012, 13(11).
48. Kovatich A.J., Kvecher L., Chen Y., Bekhash A., Hooke J.A., Shriver C.D., Mural R.J., Hu H. P3-05-02: subtype-specific co-occurrence of atypical hyperplasia and in situ carcinoma with invasive breast cancers. Cancer Res. 2011, 71(24). Supplement 3.
49. Risk assessment, . http://drholmesmd.com/resources/risk-assessment/.
50. Harvey J.A., Bovbjerg V.E. Quantitative assessment of mammographic breast density: relationship with breast cancer risk 1. Radiology 2004, 230(1):29-41.
51. Hall M., et al. The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 2009, 11(1):10-18.
52. De Maio C., Fenza G., Gaeta M., Loia V., Orciuoli F. A knowledge-based framework for emergency DSS. Knowl. Based Syst. 2011, 24(8):1372-1379.
53. Acampora G., Loia V., Vitiello A. Managing urban waste collection through timed automata based fuzzy cognitive maps. CD-ARES 2012, 501-515.
54. Homenda W., Jastrzebska A., Pedrycz W. Granular Cognitive Maps reconstruction. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2014, 2572-2579.
55. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, J. Wiley & Sons Inc, New York. 2nd edition.
56. Quinlan J.R. Decision trees and decision making. IEEE Transactions on System, Man, and Cybernetics 1990, 20(2):339-346.