Estimating survival rate of kidney transplants by using data mining

authors:

avatar Alireza Borhani , avatar Leila Shahmoradi , avatar Gholamreza Pourmand , avatar Ph. D Aghsaei fard , avatar Alireza Borhani , *

Koomesh: Vol.19, issue 2; 253-262
published online: June 01, 2017
article type: issue_documents_importer
received: April 01, 2016
accepted: December 01, 2016

how to cite: Borhani A, Shahmoradi L, Pourmand G, Aghsaei fard P D, Borhani A. Estimating survival rate of kidney transplants by using data mining. koomesh. 2017;19(2):e151077. 

Abstract

Introduction: todays, kidney failure is one of the costly problems of human society and use of renal replacement therapy is increasing in the world and Iran. Survival analysis is one of the fields in medical prognosis and data mining is a process of discovering unknown relationship and is a useful pattern from data and is known as a highly efficient method in survival analysis. Conclusively, the purpose of this study is predicting the survival of the kidney transplant patient;#39s according to variables before kidney transplant. Materials and Methods: In order to identify important factors for predicting survival in kidney transplant, informative requirements assessment was done by using self-designed questionnaire. Then, obtained information from the analysis of questionnaire was reviewed and data from 513 medical record of kidney patient in Sina Urology Research Center was extracted. Ultimately, by applying CRISP methodology, data mining was done by IBM SPSS Modeler 14.2 and C.5 algorithm. Results: In this study, BMI, ESRD and dialysis time were evaluated as the most effective factors in survival kidney transplant and extracted rules from the model can be used for predicting the survival of the transplanted kidney before the surgery. Accuracy rate of this model was estimated at 96.77%. Conclusion: The high accuracy rate of C5.0 model shows the power of it in survival prediction. Furthermore, the most effective kidney transplant survival factors were identified and kidney transplanted survival of a new patient with distinctive features, can be predicted

References

  • 1.

    WHO. Transplantation. Documentation center: http://www.who.int/transplantation/publications/en; 2008 [cited 2013 February]; Department of Essential Health Technologies (HSS/EHT/CPR)]. Available from: http://www.who.int/transplantation/gkt/statistics/en/.

  • 2.

    Mahdavi-Mazdeh M. Why do we need chronic kidney disease screening and which way to go. Iran J Kidney Dis 2010; 4: 275-281. (Persian).

  • 3.

    Nedjat S, Montazeri A, Holakouie K, Mohammad K, Majdzadeh R. Psychometric properties of the Iranian interview-administered version of the world health organization's quality of life questionnaire (WHOQOL-BREF): a population-based study. BMC Health Serv Res 2008; 8: 1. (Persian).

  • 4.

    Abbaszadeh S, Nourbala M, Taheri S, Ashraf A, Einollahi B. Renal transplantation from deceased donors in Iran. Saudi J Kidney Dis Transplant 2008; 664-668. (Persian).

  • 5.

    Mortazavi N. Bright outlook in dialysis technology in Iran. MED LAB Engine Magazine 2014; 157: 75-77. (Persian).

  • 6.

    Faraj Zade A. Principles of Urology: TUMS; 1382. (Persian(.

  • 7.

    Lofaro D, Maestripieri S, Greco R, Papalia T, Mancuco D, Conforti D, Bonofiglio R. Prediction of chronic allograft nephropathy using classication trees. Transplant Proc 2010; 42: 1130-1133.

  • 8.

    ET L. Statistical methods for survival data analysis. 2nd ed. New York: John Wiley Sons Inc; 1992.

  • 9.

    Moghadasi H, Hoseini A, Asadi F, Jahanbakhsh M. Data mining and its application in health. Health Inform Manag 2012. (Persian).

  • 10.

    Pang-Ning T, Steinbach M, Vipin K. Introduction to Data Mining Addison Wesley 2005.

  • 11.

    Hassanzadeh M, Razavi Ebrahimi A. Comparison classificaion of data mining algorithms in medical sciences. Iranian J Med Inform 2012; 2. (Persian).

  • 12.

    Alizadeh S, Malek Mohamadi S. Data mining & knowledge discovery step by step with clementine. Tehran Iran Khajeh Nasir Univ 2014. (Persian).

  • 13.

    Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining method. Artif Intell Med 2004; 27: 1-15.

  • 14.

    Liu Zea HI, Media B. Efcient support vector machine method for survival prediction with SEER data in Arabnia advances in computational biology. New York: Springer Science; 2010.

  • 15.

    Alizadeh S, Ghazanfari M, B T. Data mining and knowledge discovery. 2nd ed. Tehran Iran: Iran University of Science and Technology; 2011. (Persian).

  • 16.

    Han J, M K. Data Mining: Concepts and Techniques. 2 Kaufmann; 2006.

  • 17.

    Ameri H, Alizadeh S, Barzegari A. Knowledge extraction of diabetics' data by decision tree method. J Health Administrat 2013; 16: 58-72. (Persian).

  • 18.

    Pal NR, L J. Advanced techniques in knowledge discovery and data mining. 1nd ed. New York: Springer Science+Business Media; 2004.

  • 19.

    Greco R, Papalia T, Lofaro D, Maestripieri S, Msncuso D, Bonofiglio R. Decisional trees in renal transplant follow-up. Transplant Proc 2010; 42: 1134-1136.

  • 20.

    Montazeri M, Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh 2014; 16: 53-59. (Persian).

  • 21.

    Ashrafi M, Hamidi Beheshti M, Shahidi Sh, Ashrafi F. Application of artificial neural network to predict graft survival after kidney transplantation: Reports of 22 years follow up of 316 patients in Isfahan. Tehran Univ Med J 2009; 67: 353-359. (Persian).

  • 22.

    Hasan zadeh J, Salahi H, Rajaei far A, Zeighami B, Hashyani A. 10-year survival analysis of its influencing factors in patients with renal transplantation and transplantation from a living donor transplant center Namazi Hospital 2011; 28-39. (Persian).

  • 23.

    Almasi Hashiani A, Rajaeefard A, Hassanzade J, Salahi H. Survival analysis of renal Transplantation and its relationship with age and sex. Koomesh 2010; 11: 302-306. (Persian).

  • 24.

    Papaioannou A, Karamanis G, Rigas L, Spanos T, Z R. Determination and modelling of clinical laboratory data of healthy individuals and patients with end-stage renal failure. Central Eur J Med 2009; 4: 12.

  • 25.

    Sudha A, Gayathri P, Jaisankar N. Effective analysis and predictive model of stroke disease using classification methods. Int J Computer Appl 2012; 43: 26-31.##.