Volume 15, Issue 4 (3-2019)                   JSDP 2019, 15(4): 85-94 | Back to browse issues page

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emami N, hassani Z. Prediction and determining the effective factors on the survival transplanted kidney for five-year in imbalanced data by the meta-heuristic approach and machine learning. JSDP. 2019; 15 (4) :85-94
URL: http://jsdp.rcisp.ac.ir/article-1-684-en.html
Abstract:   (2572 Views)

Chronic kidney failure is one of the most widespread diseases in Iran and the world. In general, the disease is common in high health indexes societies due to increased longevity. Treatment for chronic kidney failure is dialysis and kidney transplantation. Kidney transplantation is an appropriate and effective strategy for patients with End-Stage Renal Disease (ESRD), and it provides a better life and reduces mortality risk for patients. In contrast to many benefits that kidney transplantation has in terms of improving physical and mental health and the life’s quality in kidney transplantation patients, it may be rejected because of host's immune response to the received kidney, and it consequences the need for another transplantation, or even death will have to. In fact, a patient that can survive for years with dialysis, he may lose his life with an inappropriate transplantation or be forced into high-risk surgical procedures.
 According to the above, the study of predicting the survival of kidney transplantation, its effective factors and providing a model for purposing of high prediction accuracy is essential. Studies in the field of survival of kidney transplantation include statistical studies, artificial intelligence and machine learning. In all of the studies in this feild, researchers have sought to identify a more effective set of features in survival of transplantation and the design of predictive models with higher accuracy and lower error rate.
This study carried out on 756 kidney transplant patients with 21 features of Imam Reza and Fourth Shahid Merab hospital in Kermanshah from 2001 to 2012. Some features set to binary value and other features have real continuous values. Due to data are unbalance, which led to convergence of classification model to majority class, so over sampling and under sampling techniques has been used for achieving higher accuracy.
To identify the more effective features on the survival of the kidney transplantation, the genetic meta-heuristic algorithm is used. For this purpose binary coding for each chromosome has been used; it is combining three single-point, two-point, and uniform operators to make better generations, better convergence and achieve higher accuracy rate. The genetic search algorithm plays a vital role in searching for such a space in a reasonable time because data search space is exponential. In fact, in balanced data, genetic algorithm determines the effective factors and the K-nearest neighbor model with precision of classification as the evaluator function was used to predict the five-year survival of the kidney transplantation. Based on the results of this study, in comparison to similar studies for prediction of survival transplanted kidney, the five-year survival rate of transplanted kidney was appropriate in these models. Also the effective factors in over sampling and under sampling methods with a precision of 96.8% and 89.2% are obtained respectively. in addition weight, donor and recipient age, pre-transplantation urea, pre-transplantation creatinine, hemoglobin before and after transplantation, donor gender, donor and recipient RH, primary illness, donor age up 30 and receipt age up 40 were identified as the effective features on kidney transplantation survival. Comparing the results of this study with previous studies shows the superiority of the proposed model from the point of view of the models' precision. In particular, balancing the data along the selection of optimal features leads to a high precision predictive model.

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Type of Study: Research | Subject: Paper
Received: 2017/12/25 | Accepted: 2018/10/6 | Published: 2019/03/8 | ePublished: 2019/03/8

1. [1] M. Mirzaei, and M. Firooz Abadi, "The impact of data mining on prediction of renal transplantation survival and identifying the effective factors on the transplanted kidney," Journal Of Health and Biomedical Informatics, Medical Informatics Re-search Center, vol. 3, pp. 1-9, 2016.
2. [2] N. Javanroh Givi, R. Alimi, H. Esmaily, M. T. Shakeri, and A. Shamsa, "Assessment of effective factors on renal transplantation estimation of rejection hazard for transplanted in Mashhad Qaem hospital," Journal of North Khorasan University of Medical Sciences, vol. 5, pp. 315-321, 2013. [DOI:10.29252/jnkums.5.2.315]
3. [3] M. Ashrafi, and et. al, "Application of artificial neural network to predict graft survival after kidney transplantation: reports of 22 years follow up of 316 patients in Isfahan," Tehran University Medical Journal, vol. 67, pp. 353-359, 2009.
4. [4] A. Almasi Hashiani, A. Rajaeefard, J . Hassanzade, and H. Salahi, "Survival analysis of renal transplantation and its relationship with age and sex," Koomesh, vol. 11, pp. 302-307, 2010.
5. [5] T. D. Noia, and et al, "An end stage kidney disease predic-tor based on an artificial neural networks ensemble," Expert Systems with App-lications, vol. 40, pp. 4438-4445, 2013. [DOI:10.1016/j.eswa.2013.01.046]
6. [6] G. Santori, I. Fontana, and U. Valente, "Applica-tion of an artificial neural network model to predict delayed decrease of serum creatinine in pediatric patients after kidney transplantation," Transplant Proc, vol. 39, pp. 1813-1819, 2007. [DOI:10.1016/j.transproceed.2007.05.026] [PMID]
7. [7] T. S. Brown, and et al, "Bayesian modeling of pre transplant variables accurately predicts kidney graft survival," Am J Nephrol, vol. 6, pp. 561-569, 2012. [DOI:10.1159/000345552] [PMID]
8. [8] J. Lasserre, S. Arnold, M. Vingron, P. Reinke, and C. P. Hinrichs, "Predicting the outcome of renal transplantation," J Am Med Inform Assoc, vol. 19, pp. 255-262, 2012. [DOI:10.1136/amiajnl-2010-000004] [PMID] [PMCID]
9. [9] A. H. Hashemian, B. beiranvand, M. Rezaei, A. Bardideh, and E. Zand-Karimi, "Comparison of artificial neural network of kidney transplant survival," International Journal of Advanced Bio-logical and biomedical Research, vol. 1, pp. 1204-1212, 2013.
10. [10] R. J. Oskouei and B. S. Bigham, "Over-sampling via under-sampling in strongly Imbalanced data," International Journal of Advanced Intelli-gence Paradigms, 2015.
11. [11] M. M. Rahman, and D. N Davis, "Addressing the class imbalance problem in medical datasets," Int J Machine Learning and Compute, vol. 2, pp. 224-228, 2013. [DOI:10.7763/IJMLC.2013.V3.307]
12. [12] N. V Chawla, "Data mining for imbalanced datasets: an overview," Data Mining Knowledge Discovery Handbook, 2005.
13. [13] Y. Sun, A. K. C. Wong, and M. S Kamel, "Classification of imbalanced data: a review," Int j patt Recogn Artif Intell, vol. 4, pp. 687-719, 2009. [DOI:10.1142/S0218001409007326]
14. [14] D. C. Li, C. W. Liu, and S. C. Hu, "A learning method for the class imbalance problem with medical datasets," J comput Bio Medi, vol. 5, pp. 509-518, 2010. [DOI:10.1016/j.compbiomed.2010.03.005] [PMID]
15. [15] F. Hoseinkhani, and B. Naser Sharif, " Two methods of converting feature based on genetic algorithms to reduce the classification error of support vector machine," Journal Of signs and data Processing, vol. 24, pp. 23-39, 2015.
16. [16] H. Hoglund, " Tax payment default prediction using genetic algorithm-based variable sele-ction," Expert Syst Appl, vol. 88, pp. 368-375, 2017. [DOI:10.1016/j.eswa.2017.07.027]
17. [17] S. Nagpal, S. Arora, S. Dey and Shreya, " Feature selection using gravitational search algorithm for biomedical data," Procedia Comput Sci, vol. 115, pp. 258-265, 2017. [DOI:10.1016/j.procs.2017.09.133]
18. [18] X. We and et. al, "Top 10 algorithms in data mining," knowl Inf Syst, vol. 14, pp. 1-37, 2008. [DOI:10.1007/s10115-007-0114-2]
19. [19] J. H. Holland, " Adaptation in natural and artifi-cial systems," University of Michigan Press, 1975.
20. [20] D. E. Goldberg, "Genetic algorithms in search optimization and mechine learning," Addison-Wesley Publishing, INC. Reading. Mass, 1989.
21. [21] S. Olariu and A. Y. Zomaya, "Handbook of bioinspired algorithms and applications," Taylor & Francis Group, LLC Press, 2006. [DOI:10.1201/9781420035063]

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