Advertisement

Screening a Case Base for Stroke Disease Detection

  • José NevesEmail author
  • Nuno Gonçalves
  • Ruben Oliveira
  • Sabino Gomes
  • João Neves
  • Joaquim Macedo
  • António Abelha
  • César Analide
  • José Machado
  • Manuel Filipe Santos
  • Henrique Vicente
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

Keywords

Stroke Disease Logic Programming Knowledge Representation and Reasoning Case Based Reasoning Similarity Analysis 

Notes

Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.

References

  1. 1.
    Go, A.S., Mozaffarian, D., Roger, V.L., Benjamin, E.J., Berry, J.D., Blaha, M.J., Dai, S., Ford, E.S., Fox, C.S., Franco, S., Fullerton, H.J., Gillespie, C., Hailpern, S.M., Heit, J.A., Howard, V.J., Huffman, M.D., Judd, S.E., Kissela, B.M., Kittner, S.J., Lackland, D.T., Lichtman, J.H., Lisabeth, L.D., Mackey, R.H., Magid, D.J., Marcus, G.M., Marelli, A., Matchar, D.B., McGuire, D.K., Mohler 3rd, E.R., Moy, C.S., Mussolino, M.E., Neumar, R.W., Nichol, G., Pandey, D.K., Paynter, N.P., Reeves, M.J., Sorlie, P.D., Stein, J., Towfighi, A., Turan, T.N., Virani, S.S., Wong, N.D., Woo, D., Turner, M.B.: On behalf of the american heart association statistics committee and stroke statistics subcommittee: heart disease and stroke statistics — 2014 update: a report from the american heart association. Circulation 129, e28–e292 (2014)CrossRefGoogle Scholar
  2. 2.
    Lindgren, A.: Risk factors. In: Norrving, B. (ed.) Oxford Textbook of Stroke and Cerebrovascular Disease, pp. 9–18. Oxford University Press, Oxford (2014)CrossRefGoogle Scholar
  3. 3.
    Shah, R.S., Cole, J.W.: Smoking and stroke: the more you smoke the more you stroke. Expert Rev. Cardiovasc. Ther. 8, 917–932 (2010)CrossRefGoogle Scholar
  4. 4.
    Hopper, I., Billah, B., Skiba, M., Krum, H.: Prevention of diabetes and reduction in major cardiovascular events in studies of subjects with prediabetes: meta-analysis of randomised controlled clinical trials. Eur. J. Cardiovasc. Prev. Rehabil. 18, 813–823 (2011)CrossRefGoogle Scholar
  5. 5.
    Khoury, J.C., Kleindorfer, D., Alwell, K., Moomaw, C.J., Woo, D., Adeoye, O., Flaherty, M.L., Khatri, P., Ferioli, S., Broderick, J.P., Kissela, B.M.: Diabetes mellitus: a risk factor for ischemic stroke in a large biracial population. Stroke 44, 1500–1504 (2013)CrossRefGoogle Scholar
  6. 6.
    Amarenco, P., Labreuche, J., Touboul, P.: High-density lipoprotein-cholesterol and risk of stroke and carotid atherosclerosis: a systematic review. Atherosclerosis 196, 489–496 (2008)CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Tuomilehto, J., Jousilahti, P., Wang, Y., Antikainen, R., Hu, G.: Total and high-density lipoprotein cholesterol and stroke risk. Stroke 43, 1768–1774 (2012)CrossRefGoogle Scholar
  8. 8.
    Grau, A.J., Barth, C., Geletneky, B., Ling, P., Palm, F., Lichy, C., Becher, H., Buggle, F.: Association between recent sports activity, sports activity in young adulthood, and stroke. Stroke 40, 426–431 (2009)CrossRefGoogle Scholar
  9. 9.
    McDonnell, M.N., Hillier, S.L., Hooker, S.P., Le, A., Judd, S.E., Howard, V.J.: Physical activity frequency and risk of incident stroke in a national US study of blacks and whites. Stroke 44, 2519–2524 (2013)CrossRefGoogle Scholar
  10. 10.
    Sealy-Jefferson, S., Wing, J.J., Sánchez, B.N., Brown, D.L., Meurer, W.J., Smith, M.A., Morgenstern, L.B., Lisabeth, L.D.: Age- and ethnic-specific sex differences in stroke risk. Gend. Med. 9, 121–128 (2012)CrossRefGoogle Scholar
  11. 11.
    Kissela, B.M., Khoury, J.C., Alwell, K., Moomaw, C.J., Woo, D., Adeoye, O., Flaherty, M.L., Khatri, P., Ferioli, S., De Los Rios La Rosa, F., Broderick, J.P., Kleindorfer, D.O.: Age at stroke: temporal trends in stroke incidence in a large, biracial population. Neurology 79, 1781–1787 (2012)CrossRefGoogle Scholar
  12. 12.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  13. 13.
    Balke, T., Novais, P., Andrade, F., Eymann, T.: From real-world regulations to concrete norms for software agents – a case-based reasoning approach. In: Poblet, M., Schild, U., Zeleznikow, J. (eds.) Proceedings of the Workshop on Legal and Negotiation Decision Support Systems (LDSS 2009), pp. 13–28. Huygens Editorial, Barcelona (2009)Google Scholar
  14. 14.
    Carneiro, D., Novais, P., Andrade, F., Zeleznikow, J., Neves, J.: Using case-based reasoning to support alternative dispute resolution. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Corchado Rodríguez, J.M. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 79, pp. 123–130. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Carneiro, D., Novais, P., Andrade, F., Zeleznikow, J., Neves, J.: Using case-based reasoning and principled negotiation to provide decision support for dispute resolution. Knowl. Inf. Syst. 36, 789–826 (2013)CrossRefGoogle Scholar
  16. 16.
    Guessoum, S., Laskri, M.T., Lieber, J.: Respidiag: a case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease. Expert Syst. Appl. 41, 267–273 (2014)CrossRefGoogle Scholar
  17. 17.
    Ping, X.-O., Tseng, Y.-J., Lin, Y.-P., Chiu, H.-J., Feipei Lai, F., Liang, J.-D., Huang, G.-T., Yang, P.-M.: A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients. Comput. Ind. 69, 12–21 (2015)CrossRefGoogle Scholar
  18. 18.
    Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)Google Scholar
  19. 19.
    Pereira, L.M., Anh, H.T.: Evolution prospection. In: Nakamatsu, K., P-W, G., Jain, L.C., Howlett, R.J. (eds.) New Advances in Intelligent Decision Technologies. SCI, vol. 199, pp. 51–63. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 annual conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)Google Scholar
  21. 21.
    Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 160–169. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh, A. (eds.) Research and Developments in Intelligent Systems XX, pp. 309–321. Springer, London (2003)Google Scholar
  23. 23.
    Machado J., Abelha A., Novais P., Neves J., Neves J.: Quality of service in healthcare units. In Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis – ETI Publication, Ghent (2008)Google Scholar
  24. 24.
    Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves J.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370. IEEE Edition (2015)Google Scholar
  25. 25.
    Neves J., Vicente, H.: A quantum approach to case-based reasoning (in Preparation)Google Scholar
  26. 26.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • José Neves
    • 1
    Email author
  • Nuno Gonçalves
    • 2
  • Ruben Oliveira
    • 2
  • Sabino Gomes
    • 2
  • João Neves
    • 3
  • Joaquim Macedo
    • 1
  • António Abelha
    • 1
  • César Analide
    • 1
  • José Machado
    • 1
  • Manuel Filipe Santos
    • 1
  • Henrique Vicente
    • 1
    • 4
  1. 1.Centro AlgorithmiUniversidade do MinhoBragaPortugal
  2. 2.Departamento de InformáticaUniversidade do MinhoBragaPortugal
  3. 3.Drs. Nicolas and AspDubaiUnited Arab Emirates
  4. 4.Departamento de Química, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal

Personalised recommendations