Identifying the Factors Affecting the Survival Rate of Kidney Transplant Patients in Isfahan Using Classification Techniques
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Abstract
Purpose: 10% of the world's population suffers from chronic kidney disease and millions of deaths occur annually due to lack of access to appropriate treatment in the world. Kidney transplantation is associated with several problems. These problems, including kidney rejection, the consequences of surgery, drug poisoning, and infectious diseases can reduce the chances of survival of these patients. The science of classification has been proposed in recent years to reduce medical errors due to inexperience, reduce the workload of physicians and provide a suitable model for making better decisions.
Materials and Methods: The data set includes information about patients for whom kidney transplantation was performed in Isfahan. The data set includes 2554 patients and 38 attributes. The techniques used in this study will include random forest, Principal Component Analysis (PCA), and Support Vector Machine (SVM).
Results: Among the studied techniques, PCA technique in three classes out of four classes had better performance than other techniques. The syndrome has the highest recurrence among traits. Five attributes include syndrome, blood type, dialysis time, weight, and age.
Conclusion: The results showed that the PCA method in the case of non-numerical data has a good performance in identifying attributes. Also, five attributes that affect the survival rate of kidney transplant patients were identified.
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17- R. Genuer, JM. Poggi, C. Tuleau-Malot, "Variable selection using random forests.", Pattern recognition letters, vol.31(14), pp.2225-36, (2010).
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22- Roshanaei G, Omidi T, Faradmal J, Safari M, Poorolajal J. "Determining affected factors on survival of kidney transplant in living donor patients using a random survival forest.", Koomesh. 10;20(3):517-23, (2018).
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24- P. Glander, K. Budde, D. Schmidt, TF. Fuller, M. Giessing, HH. Neumayer, L. Liefeldt, “The ‘blood group O problem’in kidney transplantation—time to change?.”, Nephrology Dialysis Transplantation.; vol.25(6), pp.1998-2004, (2010).
25- Manook M, Koeser L, Ahmed Z, Robb M, Johnson R, Shaw O, Kessaris N, Dorling A, Mamode N. "Post-listing survival for highly sensitised patients on the UK kidney transplant waiting list: a matched cohort analysis." The Lancet, 18;389(10070):727-34, (2017).
26- L. Resende, J. Guerra, A. Santana, C. Mil-Homens, F. Abreu, AG. da Costa. “Influence of dialysis duration and modality on kidney transplant outcomes.” In Transplantation proceedings, (Vol. 41, (3,) pp. 837-839, (2009).
27- N.Aksoy, “Weight gain after kidney transplant, Exp Clin Transplant. vol.14(3), pp.138-40, 99, (2016).
28- R. Bloodworth, K. Ward, G. Relyea, AK. Cashion, "Food availability as determinant of weight gain among renal transplant recipients.", Res Nurs Health. vol.37(3), pp.253-259, (2014).
29- KA. Andreoni, R. Forbes, RM.Andreoni, G. Phillips, H. Stewart, M. Ferris, "Age-related kidney transplant outcomes: health disparities amplified in adolescence.", JAMA internal medicine.,vol.173(16),pp.1524-32, (2013).
2- AJ. Ghods, "Renal transplantation in Iran. Nephrology Dialysis Transplantation.", vol.17(2), pp.222-8, (2002).
3- M.S. Parmar, "Kidney stone.", BMJ, vol.328 (7453), pp. 1420-4, (2004).
4- Stoller, M.L. "Urinary Stone Disease." In: Tanagho, E.A. and McAninch, J.W., Eds., Smith’s General Urology, 17th Edition, McGraw Hill, New York, 246, (2008).
5- Z. Rajabfardi H. Hatami, S.K. Khodakarim "Factors associated with end stage renal disease among hemodialysis patients in Tuyserkan City."Pajouhan Scientific Journal, (2014).
6- K N Stamatiou, V I Karanasiou, R E Lacroix, N G Kavouras, V T Papadimitriou, C Chlopsios, F A Lebren, F Sofras. "Prevalence of urolithiasis in rural Thebes.", Rural and Remote Health. vol.6(610), (2006).
7- X. Fan, S. Kalim, W. Ye, S. Zhao, Ma, SU. Nigwekar, KE. Chan, et al. “Urinary stone disease and cardiovascular disease risk in a rural Chinese population.”, Kidny IntRep. vol.2 (6), pp.1042-9, (2017).
8- Bagherian H, Haghjooy Javanmard S, Sharifi M, Sattari M. "Using data mining techniques for predicting the survival rate of breast cancer patients: a review article,", Tehran Univ Med J;79(3):176-86, (2021).
9- Moeinzadeh F, Rouhani M H, Mortazavi M, Sattari M. "Prediction of chronic kidney disease in Isfahan with extracting association rules using datamining techniques.", Tehran Univ Med J.; 79 (6) :459-467, (2021).
10- Mortazavi M, Atapour A, Mohammadi M, Sattari M. "Predicting the cause of kidney stones in patients using random forest, support vector machine and neural network.", Tehran Univ Med J.; 79 (9) :706-714, (2021).
11- L. Shahmoradi, M. Langarizadeh, G. Pourmand, P. Aghsaei fard, A. Borhani, “Estimating survival rate of kidney transplants by using data mining.”, Koomesh. vol. 19 (2): pp. 253-262, (2017).
12- M. Nematollahi, R. Akbari, S. Nikeghbalian, C. Salehnasab, “Classification models to predict survival of kidney transplant recipients using two intelligent techniques of data mining and logistic regression.”, International journal of organ transplantation medicine. vol. 8(2): pp.119, (2017).
13- N. Emami, Z. Hassani, “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, vol.15 (4), pp.85-94, (2019).
14- L Ashiku., M. Al-Amin, S. Madria, C. Dagli. "Machine Learning Models and Big Data Tools for Evaluating Kidney Acceptance." Procedia Computer Science, vol. 185, pp.177-84, (2021).
15- Tapak, L.; Hamidi, O.; Amini, P.; Poorolajal, J. "Prediction of Kidney Graft Rejection Using Artificial Neural Network.", Healthc. Inform. Res., vol. 23, 277–284, (2017).
16- Topuz, K.; Zengul, F.D.; Dag, A.; Almehmi, A.; Yildirim, M.B. "Predicting graft survival among kidney transplant recipients: A Bayesian decision support model.", Decis. Support Syst., vol. 106, 97–109, (2018)
17- R. Genuer, JM. Poggi, C. Tuleau-Malot, "Variable selection using random forests.", Pattern recognition letters, vol.31(14), pp.2225-36, (2010).
18- S. Asante-Okyere, C. Shen, YY. Ziggah, MM. Rulegeya, X. Zhu, "Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation.", Computers & Geosciences. vol.145, pp.104555, (2020).
19- L. Zhu, RA. Moro. “Support vector machines (SVM) as a technique for solvency analysis.”, (2008).
20- JA. Swets, “Measuring the accuracy of diagnostic systems.”, Science.; vol.240(4857), pp.1285-93, (1988).
21- Scheffner I, Gietzelt M, Abeling T, Marschollek M, Gwinner W. "Patient survival after kidney transplantation: important role of graft-sustaining factors as determined by predictive modeling using random survival forest analysis." Transplantation. 1;104(5):1095-107, (2020).
22- Roshanaei G, Omidi T, Faradmal J, Safari M, Poorolajal J. "Determining affected factors on survival of kidney transplant in living donor patients using a random survival forest.", Koomesh. 10;20(3):517-23, (2018).
23- H. Hohage, U. Kleyer, D. Brückner, C. August, W. Zidek, C. Spieker, "Influence of proteinuria on long-term transplant survival in kidney transplant recipients.”, Nephron. vol.75(2), pp.160-5, (1997).
24- P. Glander, K. Budde, D. Schmidt, TF. Fuller, M. Giessing, HH. Neumayer, L. Liefeldt, “The ‘blood group O problem’in kidney transplantation—time to change?.”, Nephrology Dialysis Transplantation.; vol.25(6), pp.1998-2004, (2010).
25- Manook M, Koeser L, Ahmed Z, Robb M, Johnson R, Shaw O, Kessaris N, Dorling A, Mamode N. "Post-listing survival for highly sensitised patients on the UK kidney transplant waiting list: a matched cohort analysis." The Lancet, 18;389(10070):727-34, (2017).
26- L. Resende, J. Guerra, A. Santana, C. Mil-Homens, F. Abreu, AG. da Costa. “Influence of dialysis duration and modality on kidney transplant outcomes.” In Transplantation proceedings, (Vol. 41, (3,) pp. 837-839, (2009).
27- N.Aksoy, “Weight gain after kidney transplant, Exp Clin Transplant. vol.14(3), pp.138-40, 99, (2016).
28- R. Bloodworth, K. Ward, G. Relyea, AK. Cashion, "Food availability as determinant of weight gain among renal transplant recipients.", Res Nurs Health. vol.37(3), pp.253-259, (2014).
29- KA. Andreoni, R. Forbes, RM.Andreoni, G. Phillips, H. Stewart, M. Ferris, "Age-related kidney transplant outcomes: health disparities amplified in adolescence.", JAMA internal medicine.,vol.173(16),pp.1524-32, (2013).
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| Issue | Vol 10 No 1 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v10i1.11506 | |
| Keywords | ||
| Data Mining Kidney Transplantation Survival Rate Random Forest Principal Component Analysis Support Vector Machine | ||
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How to Cite
1.
Atapour A, Sattari M, Mortazavi M. Identifying the Factors Affecting the Survival Rate of Kidney Transplant Patients in Isfahan Using Classification Techniques. Frontiers Biomed Technol. 2022;10(1):6-13.
