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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breast Cancer Organization, U.S. Breast Cancer Statistics, March 2017. http://www.breastcancer.org/symptoms/understand_bc/statistics
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)
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)
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)
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)
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)
Marlinga, C., Montanib, S., Bichindaritzc, I., Funkd, P.: Synergistic case-based reasoning in medical domains. Expert Syst. 41(2), 249–259 (2014)
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)
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)
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)
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)
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)
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)
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)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)
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)
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)
Hullermeier, E., Dubois, D., Prade, H.: Model adaptation in possibilistic instance-based reasoning. IEEE Trans. Fuzzy Syst. 10(3), 333–339 (2002)
Alsun, M.H., Lecornu, L., Solaiman, B., Le Guillou, C., Cauvin, J.M.: Medical diagnosis by possibilistic classification reasoning
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)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues. Methodol. Variations Syst. Approaches 7(1), 39–59 (1994)
Leake, D.B.: Case-Based Reasoning: Experiences, Lessons, and Future Directions. MIT Press, Cambridge (1996)
Riesbeck, K., Schank, R.C.: Inside Case-Based Reasoning. Artificial Intelligence Series, 1st Edition (1989)
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)
Gruber, T.: Towards principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)
Gruber, T.R.: A translation approach to portable ontologies. Knowl. Acquisition J. 4(5), 199–229 (1993)
Dubois, D., Prade, H.: Theory of Possibility an Approach to Computerized Processing of Uncertainty. Plenum Press, Berlin (1988)
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)
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)
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)
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)
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
Idoudi, R., Ettabaa, K.S., Solaiman, B., Mnif, N.: Association rules based ontology enrichment. Int. J. Web Appl. 8(1), 16–25 (2016)
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
Hertz, T.: Learning distance functions: algorithms and applications. Ph.D. thesis, pp. 9–14 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-65930-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65929-9
Online ISBN: 978-3-319-65930-5
eBook Packages: Computer ScienceComputer Science (R0)