Skip to main content
SpringerLink
Log in

A Soft Computing Approach to Kidney Diseases Evaluation

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Levey, A. S., and Coresh, J., Chronic kidney disease. Lancet 379:165–180, 2012.

    Article  PubMed  Google Scholar 

  2. Eckardt, K. U., Berns, J. S., Rocco, M. V., and Kasiske, B. L., Definition and classification of CKD: The debate should be about patient prognosis—a position statement from KDOQI and KDIGO. Am J Kidney Dis 53:915–920, 2009.

    Article  PubMed  Google Scholar 

  3. Chronic Kidney Disease Platform (2015) http://gid.min-saude.pt/irc.php?lang=en. Accessed 27 April 2015

  4. Jha, V., Garcia-Garcia, G., Isek, K., Li, Z., Naicker, S., Plattner, B., Saran, R., Wang, A. Y. M., and Yang, C. W., Chronic kidney disease: Global dimension and perspectives. Lancet 382:260–272, 2013.

    Article  PubMed  Google Scholar 

  5. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group: KDIGO 2012, Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 3:1–150, 2013.

    Article  Google Scholar 

  6. Levey, A. S., Atkins, R., and Coresh, J., Chronic kidney disease as a global public health problem: Approaches and initiatives—a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 72:247–59, 2007.

    Article  CAS  PubMed  Google Scholar 

  7. Singh, P., Rifkin, D. E., and Blantz, R. C., Chronic kidney disease: An inherent risk factor for acute kidney injury? (Mini-Review). Clin J Am Soc Nephrol 5:1690–1695, 2010.

    Article  PubMed  Google Scholar 

  8. Bydash, J. R., and Ishani, A., Acute kidney injury and chronic kidney disease: A work in progress. Clin J Am Soc Nephrol 6:2555–2557, 2011.

    Article  CAS  PubMed  Google Scholar 

  9. James, M. T., Hemmelgarn, B. R., and Tonelli, M., Early recognition and prevention of chronic kidney disease. Lancet 375:1296–1309, 2010.

    Article  CAS  PubMed  Google Scholar 

  10. Hemmelgarn, B. R., Manns, B. J., Lloyd, A., James, M. T., Klarenbach, S., Quin, R. R., Wiebe, N., Tonelli, M., and for the Alberta Kidney Disease Network, Relation between kidney function, proteinuria, and adverse outcomes. J Am Med Assoc 303:423–429, 2010.

    Article  CAS  Google Scholar 

  11. Inker, L. A., Schmid, C. H., Tighiouart, H., Eckfeldt, J. H., Feldman, H. I., Greene, T., Kusek, J. W., Manzi, J., Van Lente, F., Zhang, Y. L., Coresh, J., and Levey, A. S., Estimating glomerular filtration rate from serum creatinine and Cystatin C. N Engl J Med 367:20–99, 2012.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Praga, M., Hernandez, E., Herrero, J. C., Morales, E., Revilla, Y., Diaz-Gonzalez, R., and Rodicio, J. L., Influence of obesity on the appearance of Proteinuria and renal insufficiency after unilateral Nephrectomy. Kidney Int 58:2111–2118, 2000.

    Article  CAS  PubMed  Google Scholar 

  13. National Institute for Health and Care Excellence (2015) Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. NICE clinical guideline 181. http://www.nice.org.uk/guidance/cg181/resources/guidance-lipid-modification-cardiovascular-risk-assessment-and-the-modification-of-blood-lipids-for-the-primary-and-secondary-prevention-of-cardiovascular-disease-pdf. Accessed 23 April 2015

  14. Locatelli, F., Aljama, P., Bárány, P., Canaud, B., Carrera, F., Eckardt, K. U., Horl, W. H., Macdougal, I. C., Macleod, A., Wiecek, A., and Cameron, S., Revised European best practice guidelines for the management of anaemia in patients with chronic renal failure. Nephrol Dial Transplant 19(supplement 2):ii44–ii47, 2004.

    Google Scholar 

  15. Gansevoort, R. T., Correa-Rotter, R., Hemmelgarn, B. R., Jafar, T. H., Heerspink, H. J. L., Mann, J. F., Matsushita, K., and Wen, C. P., Chronic kidney disease and cardiovascular risk: Epidemiology, mechanisms, and prevention. Lancet 382:339–352, 2013.

    Article  PubMed  Google Scholar 

  16. Yach, D., Hawkes, C., Gould, C. L., and Hofman, K. J., The global burden of chronic diseases: overcoming impediments to prevention and control. JAMA 291:2616–2622, 2004.

    Article  CAS  PubMed  Google Scholar 

  17. Blix, H. S., Viktil, K. K., Moger, T. A., and Reikvam, A., Use of renal risk drugs in hospitalized patients with impaired renal function—an underestimated problem? Nephrol Dial Transplant 21:3164–3171, 2006.

    Article  PubMed  Google Scholar 

  18. Tawadrous, D., Shariff, S. Z., Haynes, R. B., Iansavichus, A. V., Jain, A. K., and Garg, A. X., Use of clinical decision support systems for kidney-related drug prescribing: A systematic review. Am J Kidney Dis 58:903–914, 2011.

    Article  PubMed  Google Scholar 

  19. Shemeikkaa, T., Bastholm-Rahmnerb, P., Elinderd, C.-G., Végc, A., Tornqvista, E., Corneliusa, B., and Korkmaza, S., A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: Design, development and proof of concept. Int J Med Inform 84:387–395, 2015.

    Article  Google Scholar 

  20. Terrell, K. M., Perkins, A. J., Hui, S. L., Callahan, C. M., Dexter, P. R., and Miller, D. K., Computerized decision support for medication dosing in renal insufficiency: A randomized, controlled trial. Ann Emerg Med 56:623–629, 2010.

    Article  PubMed  Google Scholar 

  21. Wei, C.-K., Su, S., and Yang, M.-C., Application of data mining on the development of a disease distribution map of screened community residents of Taipei County in Taiwan. J Med Syst 36:2021–2027, 2012.

    Article  PubMed  Google Scholar 

  22. Di Noia, T., Ostuni, V. C., Pesce, F., Binetti, G., Naso, D., Schena, F. P., and Di Sciascio, E., An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40:4438–4445, 2013.

    Article  Google Scholar 

  23. Akgundogdu, A., Kurt, S., Kilic, N., Ucan, O. N., and Akalin, N., Diagnosis of renal failure disease using adaptive neuro-fuzzy inference. Syst J Med Syst 34:1003–1009, 2010.

    Article  PubMed  Google Scholar 

  24. Neves, J., A logic interpreter to handle time and negation in logic databases. In: Muller, R. L., and Pottmyer, J. J. (Eds.), Proceedings of the annual conference of the ACM on the fifth generation challenge. Association for Computing Machinery, New York, pp. 50–54, 1984.

    Google Scholar 

  25. Neves, J., Machado, J., Analide, C., Abelha, A., and Brito, L., The halt condition in genetic programming. In: Neves, J., Santos, M. F., and Machado, J. (Eds.), Progress in artificial intelligence, LNAI, vol. 4874. Springer, Berlin, pp. 160–169, 2007.

    Chapter  Google Scholar 

  26. Cortez, P., Rocha, M., and Neves, J., Evolving time series forecasting ARMA models. J Heuristics 10:415–429, 2004.

    Article  Google Scholar 

  27. Hong, T., Hart, K., Soh, L.-K., and Samal, A., Using spatial data support for reducing uncertainty in geospatial applications. GeoInformatica 18:63–92, 2014.

    Article  Google Scholar 

  28. Li, R., Bhanu, B., Ravishankar, C., Kurth, M., and Ni, J., Uncertain spatial data handling: Modeling, indexing and query. Comput Geosci 33:42–61, 2007.

    Article  CAS  Google Scholar 

  29. Schneider, M., Uncertainty management for spatial data in databases: Fuzzy spatial data types. In: Guting, R. H., Dimitris Papadias, D., and Lochovsky, F. (Eds.), Advances in Spatial Databases, LNCS, vol. 1651. Springer, Berlin, pp. 330–351, 1999.

    Chapter  Google Scholar 

  30. Freire, L., Roche, A., and Mangin, J.-F., What is the best similarity measure for motion correction in fMRI time series? IEEE Trans Med Imaging 21:470–484, 2002.

    Article  CAS  PubMed  Google Scholar 

  31. Liao, T., Clustering of time series data?—a survey. Pattern Recog 38:1857–1874, 2005.

    Article  Google Scholar 

  32. Gelfond, M., and Lifschitz, V., The stable model semantics for logic programming. In: Kowalski, R., and Bowen, K. (Eds.), Logic Programming – Proceedings of the Fifth International Conference and Symposium, pp. 1070–1080, 1988.

  33. Kakas, A., Kowalski, R., and Toni, F., The role of abduction in logic programming. In: Gabbay, D., Hogger, C., and Robinson, I. (Eds.), Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5. Oxford University Press, Oxford, pp. 235–324, 1998.

    Google Scholar 

  34. Pereira, L. M., and Anh, H. T., Evolution prospection. In: Nakamatsu, K. (Ed.), New Advances in Intelligent Decision Technologies—Results of the First KES International Symposium IDT 2009, Studies in Computational Intelligence, vol. 199. Springer, Berlin, pp. 51–64, 2009.

    Google Scholar 

  35. Lucas, P., Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., and Mackintosh, A. (Eds.), Research and Developments in Intelligent Systems XX. Springer, London, pp. 309–321, 2004.

    Chapter  Google Scholar 

  36. Machado, J., Abelha, A., Novais, P., and Neves, J., Quality of service in healthcare units. Int J Comput Aided Eng Technol 2:436–449, 2010.

    Article  Google Scholar 

  37. Neves, J., Martins, M. R., Vicente, H., Neves, J., Abelha, A., and Machado, J., An assessment of chronic kidney diseases. In: Rocha, Á., Correia, A. M., Costanzo, S., and Reis, L. P. (Eds.), New Contributions in Information Systems and Technologies—1, Advances in Intelligent Systems and Computing, vol. 353. Springer International Publishing, Cham, pp. 179–191, 2015.

    Google Scholar 

  38. Vicente, H., Dias, S., Fernandes, A., Abelha, A., Machado, J., and Neves, J., Prediction of the quality of public water supply using artificial neural networks. J Water Supply Res Technol AQUA 61:446–459, 2012.

    Article  CAS  Google Scholar 

  39. Vicente, H., Couto, C., Machado, J., Abelha, A., and Neves, J., Prediction of Water Quality Parameters in a Reservoir using Artificial Neural Networks. Int J Des Nat Ecodynamics 7:309–318, 2012.

    Article  Google Scholar 

  40. Vicente, H., Roseiro, J., Arteiro, J., Neves, J., and Caldeira, A. T., Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks. Can J Forest Res 43:985–992, 2013.

    Article  CAS  Google Scholar 

  41. Carneiro, D., Novais, P., Andrade, F., Zeleznikow, J., and Neves, J., Using case-based reasoning and principled negotiation to provide decision support for dispute resolution. Knowl Inf Syst 36:789–826, 2013.

    Article  Google Scholar 

  42. Mendes, R., Kennedy, J., and Neves, J., The fully informed particle swarm: Simpler, maybe better. IEEE Trans Evol Comput 8:204–210, 2004.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Algoritmi, Universidade do Minho, 4710-057, Braga, Portugal

    José Neves, Sabino Gomes, António Abelha & José Machado

  2. Departamento de Química, ICAAM, Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671, Évora, Portugal

    M. Rosário Martins & João Vilhena

  3. Drs. Nicolas & Asp, Dubai, United Arab Emirates

    João Neves

  4. Departamento de Química, Centro de Química de Évora, Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671, Évora, Portugal

    Henrique Vicente

Authors
  1. José Neves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Rosário Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Vilhena
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. João Neves
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabino Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. António Abelha
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. José Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Henrique Vicente
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Neves.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neves, J., Martins, M.R., Vilhena, J. et al. A Soft Computing Approach to Kidney Diseases Evaluation. J Med Syst 39, 131 (2015). https://doi.org/10.1007/s10916-015-0313-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0313-4

Keywords

Search

Navigation