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Ontology Based Possibilistic Reasoning for Breast Cancer Aided Diagnosis

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Information Systems (EMCIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 299))

Abstract

Medical diagnosis is a very complex task in the case where information suffer from various imperfections. That’s why doctors rely on their knowledge and previous experiences to take the adequate decision. In this context, the case based reasoning (CBR) paradigm aims to resolve current problems basing on previous knowledge. Using ontologies to store and represent the background knowledge may notably enhance and improve the CBR semantic effectiveness. This paper proposes a possibilistic ontology based CBR approach in order to perform a possibilistic semantic retrieval algorithm that handles ambiguity and uncertainty problems. The approach is implemented and tested on the mammographic domain. The target ontology is instantiated with 113 real cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies.

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References

  1. Breast Cancer Organization, U.S. Breast Cancer Statistics, March 2017. http://www.breastcancer.org/symptoms/understand_bc/statistics

  2. Begum, S., Ahmed, M., Funk, P., Xiong, N., Folke, M.: Case-based reasoning systems in the health sciences: a survey of recent trends and developments. IEEE Trans. Syst. Man Cybern. 7(1), 39–59 (2010)

    Google Scholar 

  3. Zia, S.S., Akhtar, P., Javid, T., Mughal, A., Mala, I.: Case retrieval phase of case-based reasoning technique for medical diagnosis. World Appl. Sci. J. 32(3), 451–458 (2014)

    Google Scholar 

  4. Boroczky, L., Simpson, M., Abe, H., Drysdale, J.: Observer study of a prototype clinical decision support system for breast cancer diagnosis using dynamic contrast-enhanced MRI. Am. J. Roentgenol. 200, 277–283 (2013)

    Article  Google Scholar 

  5. Darzi, M., AsgharLiaei, A., Hosseini, M., Asghari, H.: Feature selection for breast cancer diagnosis: a case-based wrapper approach. Int. J. Med. Health Biomed. Bioeng. Pharmaceutical Eng. 5(5), 220–223 (2011)

    Google Scholar 

  6. Sharaf-elDeen, D.A., Moawad, I.F., Khalifa, M.E.: A breast cancer diagnosis system using hybrid casebased approach. Int. J. Comput. Appl. 72(23), 14–19 (2013)

    Google Scholar 

  7. Marlinga, C., Montanib, S., Bichindaritzc, I., Funkd, P.: Synergistic case-based reasoning in medical domains. Expert Syst. 41(2), 249–259 (2014)

    Article  Google Scholar 

  8. Dendani, N., Khadir, M., Guessoum, S.: Use a domain ontology to develop knowledge intensive CBR systems for fault diagnosis. In: International Conference on Information Technology and e-Services (ICITeS), pp. 1–6 (2012)

    Google Scholar 

  9. Amailef, K., Lu, J.: Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decis. Support Syst. 55(1), 79–97 (2013)

    Article  Google Scholar 

  10. Garrido, J.L., Hurtado, M.V., Noguera, M., Zurita, J.M.: Using a CBR approach based on ontologies for recommendation and reuse of knowledge sharing in decision making. In: Eighth International Conference on Hybrid Intelligent Systems, pp. 837–842 (2008)

    Google Scholar 

  11. Samwald, M., Antonio, J., Giménez, M., Boyce, R., Freimuth, R., Adlassnig, K.P.: Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies. BMC Med. Inf. Decis. Mak. 15, 12 (2015)

    Google Scholar 

  12. El-Sappagh, S., Elmogy, M., El-Masri, S., Riad, A.: A diabetes diagnostic domain ontology for CBR system from the conceptual model of SNOMED CT. In: The Second International Conference on Engineering and Technology, pp. 1–7 (2014)

    Google Scholar 

  13. El-Sappagh, S., Elmogy, M., Riad, A., Zaghloul, H., Badria, F.: A proposed SNOMED CT ontology-based encoding methodology for diabetes diagnosis case-base. In: The Ninth International Conference on Computer Engineering and Systems, pp. 184–191 (2014)

    Google Scholar 

  14. Amin, E., Abdrabou, M.L., Salem, A.M.: A breast cancer classifier based on a combination of case-based reasoning and ontology approach. In: International Multiconference on Computer Science and Information Technology, pp. 3–10 (2010)

    Google Scholar 

  15. Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  16. Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  17. Abdul, M., Muhammad, A., Mustapha, N., Muhammad, S., Ahmad, N.: Database workload management through CBR and fuzzy based characterization. Appl. Soft Comput. 22, 605–621 (2014)

    Article  Google Scholar 

  18. Ekong, V., Inyang, U., Onibere, E.: Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Appl. Sci. 6(7), 79–88 (2012)

    Google Scholar 

  19. Hullermeier, E., Dubois, D., Prade, H.: Model adaptation in possibilistic instance-based reasoning. IEEE Trans. Fuzzy Syst. 10(3), 333–339 (2002)

    Article  Google Scholar 

  20. Alsun, M.H., Lecornu, L., Solaiman, B., Le Guillou, C., Cauvin, J.M.: Medical diagnosis by possibilistic classification reasoning

    Google Scholar 

  21. El-Sappagha, S., Elmogy, M., Riadc, A.M.: A fuzzy-ontology oriented case-based reasoning framework for semantic diabetes diagnosis. Artif. Intell. 65(3), 179–208 (2015)

    Article  Google Scholar 

  22. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues. Methodol. Variations Syst. Approaches 7(1), 39–59 (1994)

    Google Scholar 

  23. Leake, D.B.: Case-Based Reasoning: Experiences, Lessons, and Future Directions. MIT Press, Cambridge (1996)

    Google Scholar 

  24. Riesbeck, K., Schank, R.C.: Inside Case-Based Reasoning. Artificial Intelligence Series, 1st Edition (1989)

    Google Scholar 

  25. Manzoor, U., Balubaid, M.A., Zafar, B., Umar, H., Khan, M.S.: Semantic image retrieval: an ontology based approach. Int. J. Adv. Res. Artif. Intell. 1(4), 1–8 (2015)

    Google Scholar 

  26. Gruber, T.: Towards principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)

    Article  Google Scholar 

  27. Gruber, T.R.: A translation approach to portable ontologies. Knowl. Acquisition J. 4(5), 199–229 (1993)

    Google Scholar 

  28. Dubois, D., Prade, H.: Theory of Possibility an Approach to Computerized Processing of Uncertainty. Plenum Press, Berlin (1988)

    Google Scholar 

  29. Dubois, D., Prade, H.: An alternative approach to the handling of subnormal possibility distributions: a critical comment on a proposal by Yager. Fuzzy Sets Syst. 24(1), 123–126 (1987)

    Article  Google Scholar 

  30. Ben Salem, Y., Idoudi, R., Hamrouni, K., Soleiman, B., Bousetta, S.: Image based ontology learning. In: 11th International Conference on Intelligent Systems: Theories and Applications, pp. 1–5 (2016)

    Google Scholar 

  31. Branici, A.: Représentation et raisonnement formels pour le pronostic basé sur l’imagerie médicale microscropique. Application à la graduation du cancer du sein. Ph.D. thesis, Université de Franche-Comté (2010)

    Google Scholar 

  32. Bulu, H., Alpkocak, A., Balci, P.: Uncertainty modeling for ontology-based mammography annotation with intelligent bi-rads scoring. Comput. Biol. Med. 43(4), 301–311 (2013)

    Article  Google Scholar 

  33. Taylor, P., Toujilov, I.: Mammographic knowledge representation in description logic. In: Riaño, D., Teije, A., Miksch, S. (eds.) KR4HC 2011. LNCS, vol. 6924, pp. 158–169. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27697-2_12

    Chapter  Google Scholar 

  34. Idoudi, R., Ettabaa, K.S., Solaiman, B., Mnif, N.: Association rules based ontology enrichment. Int. J. Web Appl. 8(1), 16–25 (2016)

    Google Scholar 

  35. Jenhani, I., Ben Amor, N., Elouedi, Z., Benferhat, S., Mellouli, K.: Information affinity: a new similarity measure for possibilistic uncertain information. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS, vol. 4724, pp. 840–852. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75256-1_73

    Chapter  Google Scholar 

  36. Hertz, T.: Learning distance functions: algorithms and applications. Ph.D. thesis, pp. 9–14 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ecole Nationale d’Ingénieurs de Tunis, BP 37, Le Belvedere, 1002, Tunis, Tunisia

    Yosra Ben Salem, Rihab Idoudi & Kamel Hamrouni

  2. IMT Atlantique, 655 Avenue du Technopôle, 29200, Plouzané, France

    Yosra Ben Salem, Rihab Idoudi, Karim Saheb Ettabaa & Basel Solaiman

Authors
  1. Yosra Ben Salem
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  2. Rihab Idoudi
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  3. Karim Saheb Ettabaa
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  4. Kamel Hamrouni
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  5. Basel Solaiman
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Correspondence to Yosra Ben Salem .

Editor information

Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Marinos Themistocleous

  2. Department of Management and Technology, Bocconi University, Milan, Italy

    Vincenzo Morabito

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Ben Salem, Y., Idoudi, R., Saheb Ettabaa, K., Hamrouni, K., Solaiman, B. (2017). Ontology Based Possibilistic Reasoning for Breast Cancer Aided Diagnosis. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_29

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  • DOI: https://doi.org/10.1007/978-3-319-65930-5_29

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