Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment

Journal of Biomedical Informatics

Volumn 45, Issue 6, 2012, Pages 1021-1034

  Tatari, Farzaneh ^a   Akbarzadeh T, Mohammad R ^a   Sabahi, Ahmad ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Breast cancer; Fuzzy probability; Fuzzy probabilistic multi agent system; Insurance premium; Uncertainty

Indexed keywords

AGENT-BASED SYSTEMS; BREAST CANCER; BREAST CANCER RISK ASSESSMENTS; DISTRIBUTED APPROACHES; FUZZY AGENTS; FUZZY COMPUTATIONS; FUZZY PROBABILITY; INSURANCE PREMIUMS; LINGUISTIC INFORMATION; LINGUISTIC VARIABLE; MULTI AGENT SYSTEM (MAS); PROBABILISTIC COMPUTING; RISK FACTORS; SOFT INFORMATION; UNCERTAINTY;

DISEASES; FUZZY SET THEORY; INSURANCE; LINGUISTICS; PROBABILITY; RISK ASSESSMENT;

MULTI AGENT SYSTEMS;

ARTICLE; BREAST CANCER; CANCER RISK; FUZZY SYSTEM; HEALTH INSURANCE; HUMAN; MEDICAL DECISION MAKING; PRIORITY JOURNAL; RISK ASSESSMENT; RISK FACTOR;

BREAST NEOPLASMS; FEMALE; FUZZY LOGIC; HUMANS; INSURANCE, HEALTH; PROBABILITY; RISK ASSESSMENT;

EID: 84869866258     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2012.05.004     Document Type: Article

References (77)

1. Zadeh LA. Possibility theory and soft data analysis. In: Cobb L, Thrall RM, editors. mathematical frontiers of the social and policy sciences. Boulder, Colorado: Westview Press; 1981. p. 69-129.
2. Zadeh L.A. Soft computing and fuzzy logic. IEEE Softw 1994, 11(6):48-56.
3. Zadeh L.A. Fuzzy probabilities. Inf Process Manage 1984, 20(3):363-372.
4. Buckley JJ. Fuzzy probabilities, new approach and applications. Springer; 2005.
5. Zadeh L.A. Fuzzy sets. Inf Control 1965, 8(3):338-353.
6. Tatari F, Akbarzadeh-T M-R. Intensity assessment of pedestrian collisions in city of Mashhad based on fuzzy probabilities. IFSA2009, Portugal; 2009.
7. Dunyak JP, Wunsch D. Fuzzy probability for system reliability. In: Proceedings of the 37th IEEE conference on decision & control Tampa; 1998.
8. Dunyak JP et al. A theory of independent fuzzy probability for system reliability. In: IEEE transactions on fuzzy systems, vol. 7, no. 2; 1999.
9. Kenarangui R. Event-tree analysis by fuzzy probability. IEEE Trans Reliab 1991;40(1).
10. Zhou J. Reliability assessment method for pressure piping containing circumferential defects based on fuzzy probability. Int J Press Vessels Pip 2005, 82:669-678.
11. Hüllermeier Karimi E. Risk assessment system of natural hazards: a new approach based on fuzzy probability. Fuzzy Sets Syst 2007, 158:987-999.
12. Yu D., Park W.S. Combination and evaluation of expert opinions characterized in terms of fuzzy probabilities. Ann Nucl Energy 2000, 27:713-726.
13. Zadeh L.A. Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. J Stat Plann Infer 2002, 105:233-264.
14. Wang G, Mao S, He Y. Optimal decision fusion with fuzzy priori probabilities. In: Proceedings of IBP; 1998.
15. Wang G, Mao S, He Y. Distributed detection fusion with fuzzy priori probabilities and fuzzy cost function. In: IEEE international radar conference; 2000.
16. Ralescu L., Ralescu D.A., Yamakata Yoko Inference by aggregation of evidence with applications to fuzzy probabilities. Inf Sci 2007, 177:378-387.
17. Huang Z, Gedeon TD. Information retrieval estimation via fuzzy probability. In: World automation congress (WAC); 2006.
18. Buckley J.J., et al. Fuzzy mathematics in economics and engineering 2002, Physica-Verlag, Heidelberg, Germany.
19. Kim J.-H., Vadakkepat P. Multi-agent systems: a survey from the robot-soccer perspective. Intell Autom Soft Comput 2000, 6(1):3-17.
20. Wooldridge M. An introduction to multi agent systems. Willey; 2002.
21. Layde PM, Webster LA, Baughman AL, Wingo PA, Rubin GL, Ory HW, The Center and Steroid Hormone Study Group. The independent associations of parity, age at first full term pregnancy and duration of breast feeding with risk of breast cancer. J Clin Epidemiol 1989;42(IO):963-73 [Printed in Great Britain].
22. Tatari F, Akbarzadeh-T M-R, Azemi A. Agent-based centralized fuzzy Kalman filtering for uncertain stochastic estimation, ICSCCW, Cyprus; 2009.
23. Corchado Juan M, Bajo J, de Paz Y, Tapia DI. Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Supp Syst 2008;44:382-96.
24. Laleci GB, Dogac A, Olduz M, Tasyurt I, Yuksel M, Okcan A. SAPHIRE: a multi-agent system for remote healthcare monitoring through computerized clinical guidelines, SAPHIRE project by the Scienti_c and Technical Research Council of Turkey, Project No.: EEEAG 105E133; 2007.
25. Jun Su C. Mobile multi-agent based, distributed information platform (MADIP) for wide-area e-health monitoring. Comput Ind 2008, 59:55-68.
26. Uckun S. Intelligent systems in patient monitoring and therapy management, a survey of research projects; 1993.
27. Zhao X., Yuan S., Yu Z., Ye W., Cao J. Designing strategy for multiagent system based large structural health monitoring. Expert Syst Appl 2008, 34:1154-1168.
28. González-Vélez H, Mier M, Julià-Sapé M, Arvanitis TN, García-Gómez JM, Robles M et al. Health agents: distributed multi-agent brain tumor diagnosis and prognosis. Springer Science+Business Media, LLC; 2007.
29. Klüver J, Stoica C, Unland R. Multi-agent system and soft computing technologies as a fruitful symbiosis for reliable medical diagnoses. In: BISC FLINT-CIBI international joint workshop on soft computing for internet and bioinformatics. US: University of Berkeley; 2003 [December 15-19].
30. Laszlo BI. Cooperative medical diagnoses elaboration by physicians and artificial agents, emergent properties in natural and artificial, complex systems; 2007.
31. Unland R, Ulieru M. Swarm Intelligence and the Holonic Paradigm: a promising symbiosis for a medical diagnostic system, KES, vol. 2; 2005.
32. Nguyen MT, Fuhrer P, Pasquier-Rocha J. Enhancing e-health information systems with agent technology. 1700 Fribourg, Switzerland: University of Fribourg, Department of Computer Science; 2008.
33. Tian J, Tianfield H. A multi-agent approach to the design of an E-medicine system. Berlin Heidelberg: Springer-Verlag; 2003.
34. Ulieru M., Hadzic M., Chang E. Soft computing agents for e-Health in application to the research and control of unknown diseases. Inf Sci 2006, 176:1190-1214.
35. Coltell O, Arregui M, Falomir Z, Corella D, Ordovas JM. Multi-agent approaches for biomedical informatics applications in the health framework: genomic epidemiology and nutrigenomics. Bio Info Genomica subgroup of the systems Integration and Re-Engineering Research Group (IRIS). Universitat Jaume I. Avda. Vicent Sos Baynat, s/n. 12071-Castellón, Spain; 2005.
36. Linard C., Ponc N., Fontenille D., Lambin E.F. A multi-agent simulation to assess the risk of malaria re-emergence in southern France. Ecol Model 2009, 220:160-174.
37. Edwards QT, Palomares MR. Assessment of risk for breast cancer utilizing history & quantitative models in primary care. J Nurse Pract JNP; 2008. p. 361.
38. Orgun B., Vub J. HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comput Biol Med 2006, 36:817-836.
39. Sutton D., Patkar V. Common KADS analysis and description of a knowledge based system for the assessment of breast cancer. Expert Syst Appl 2009, 36:2411-2423.
40. Fletcher SW, Hayes DF, Dizon DS. <> (accessed 2010). http://www.uptodate.com/contents/patient-information-risk-factors-for-breast-cancer.
41. Hulka B.S., Moorman P.G. Reprint of breast cancer: hormones and other risk factors. Maturitas 2008, 61(1-2):203-213.
42. Mackay J., Schulz P., Rubinelli S., Pithers A. Online patient education and risk assessment: project OPERA from cancer backup putting inherited breast cancer risk information into context using argumentation theory. Patient Educ Couns 2007, 67:261-266.
43. Park K, Chang SJ, Chang Kim H, Cheol z Park E, Sook Lee E, Mo Nam C. Big gap between risk perception for breast cancer and risk factors: nationwide survey in Korea. Patient Education and Counseling; 2009.
44. Chen S.J., Chen S.M. A new method for handling multi criteria fuzzy decision-making problems using FN-IOWA operators. Cyber Syst 2003, 34(2):109-197.
45. Bellcross C. Approaches to applying breast cancer risk prediction models in clinical practice. Oncol Genet 2009;6(8).
46. Dipiro JT, Tablert RL et al. Pharmacotherapy: a pathophysiologic approach. 5th ed. New York: Mc Graw-Hill; 2002. p. 2223-51.
47. Devita VT, Hellman S, Rosenberg SA. Cancer: principles and practice of oncology. 7th ed. Philadelphia: Lippincott Williams and Wilkins; 2005. p. 1415-25.
48. Carmichael A.S., Sami J.M. Dixon, breast cancer risk among the survivors of atomic bomb and patients exposed to therapeutic ionizing radiation. EJSO 2003, 29:475-479.
49. Santen R.J., Boyd N.F., Chlebowski R.T., et al. Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. Endocr Relat Cancer 2007, 14:169-187.
50. Bassett LW, Hendrick RE, Bassford T, Butler PF, Carter D et al. Quality determinants of mammography. Rockville, MD: US Department of Health and Human Services, Agency for Health Care Policy and Research; 1994.
51. Gammon M.D., John E.M., Britton J.A. Recreational and occupational physical activities and risk of breast cancer. J Natl Cancer Inst 1998, 90:100-117.
52. Bell J. The new genetics in clinical practice. BMJ 1998, 316:618-620.
53. Beaudet A.L. ASHG presidential address: making genomic medicine a reality. Am J Hum Genet 1998, 64(1999):1-13.
54. Lichtenstein P., Holm N.V., Verkasalo P.K., et al. Environmental and heritable factors in the causation of cancer-analyses of cohorts of twins from Sweden, Denmark and Finland. N Engl J Med 2000, 343:78-85.
55. Peto J., Mack T.M. High constant incidence in twins and other relatives of women with breast cancer. Nat Genet 2000, 26:411-414.
56. Easton D.F., Pooley K.A., Dunning A.M., et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007, 447:1087-1093.
57. Hunter D.J., Kraft P., Jacobs P., et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007, 39:870-874.
58. Stacey S.N., Manolescu A., Sulem P., et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 2007, 39:865-869.
59. Turgut S., Yaren A., Kursunluoglu R., Turgut G. MDR1 C3435T polymorphism in patients with breast cancer. Arch Med Res 2007, 38:539-544.
60. Pharoah P, Antoniou AC, Easton DF, Ponder B. Polygenes, risk prediction, and targeted prevention of breast cancer. New Engl J Med; 2008 [June 26].
61. Gail M.H., Brinton L.A., Byar D.P., et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989, 81:1879-1886.
62. Claus E.B., Risch N., Thompson W.D. Autosomal dominant inheritance of early-onset breast cancer: implications for risk prediction. Cancer 1994, 73:643-651.
63. Tyrer J., Duffy S.W., Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004, 23:1111-1130.
64. Chen S.J., Chen S.M. Fuzzy risk analysis based on similarity of generalized fuzzy numbers. IEEE Trans Fuzzy Syst 2003, 11(1):45-56.
65. Cheng C.H. A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst 1998, 95(3):307-317.
66. Murakami S, Maeda S, Imamura S. Fuzzy decision analysis on the development of a centralized regional energy control system. In: Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, Tokyo, Japan; 1983. p. 363-8.
67. Yager S. On a general class of fuzzy connectives. Fuzzy Sets Syst 1980, 4(3):235-242.
68. Yong D., Qi L. A TOPSIS-based centroid-index ranking method of fuzzy numbers and its application in decision-making. Cyber Syst 2005, 36(6):581-595.
69. Chen S.-M., Wang C.-H. Fuzzy risk analysis based on ranking fuzzy numbers using a-cuts, belief features and signal/noise ratios. Expert Syst Appl 2009, 36:5576-5581.
70. Chen L.H., Lu H.W. An approximate approach for ranking fuzzy numbers based on left and right dominance. Comput Math Appl 2001, 41(12):1589-1602.
71. Lu H.W., Wang C.B. An index for ranking fuzzy numbers by belief feature. Inf Manage Sci 2005, 16(3):57-70.
72. Chen L.H., Lu H.W. The preference order of fuzzy numbers. Comput Math Appl 2002, 44(10):1455-1465.
73. Matiaa F., Jiméneza A., Al-Hadithia B.M., Rodriguez-Losada D., Gallna R. The fuzzy Kalman filter: state estimation using possibilistic techniques. Fuzzy Sets Syst 2006, 157:2145-2170.
74. Armstrong K., Weber B., FitzGerald G., Hershey J.C., Pauly M.V., Lemaire J., et al. Life insurance and breast cancer risk assessment: adverse selection. Genet Test Decision Discrim Am J Med Genet 2003, 120A:359-364.
75. Lemaire J, Subramanian K, Armstrong K, Asch DA. Pricing term insurance in the presence of family history of breast or ovarian cancer. North Am Actuar J 2000;4(2).
76. Ward E., Halpern M., Schrag N., Cokkinides V., DeSantis C., Bandi P., et al. Association of insurance with cancer care utilization and outcomes. CA Cancer J Clin 2008, 58:9-31.
77. Mousavi S.-M., Montazeri A., Mohagheghi M.-A., Mousavi-J A., Harirchi I., Najafi M., et al. Breast cancer in Iran: an epidemiological review. Breast J 2007, 13(4):383-391.