Koomesh
Journal of Semnan University of Medical Sciences
Outlines
Estimating survival rate of kidney transplants by using data mining
Abstract
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.##.
Copyright
© 2017, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
Similar Articles
Determining affected factors on survival of kidney transplant in living donor patients using a random survival forest
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. 2018;20(3):e152988. doi:
Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models
Khosravi B, Pourahmad S, Bahreini A, Nikeghbalian S, Mehrdad G. Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models. Hepat Mon. 2015;15(9):e25164. doi: https://doi.org/10.5812/hepatmon.25164
Longitudinal Assessment of Serum Creatinine Levels on Graft Survival After Renal Transplantation: Joint Modeling Approach
Maraghi E, Rahimi Foroushani A, Younespour S, Rostami Z, Einollahi B, et al. Longitudinal Assessment of Serum Creatinine Levels on Graft Survival After Renal Transplantation: Joint Modeling Approach. Nephro-Urol Mon. 2016;8(4):e37666. doi: https://doi.org/10.5812/numonthly.37666
Chronic Graft Loss and Death in Patients With Post-Transplant Malignancy in Living Kidney Transplantation: A Competing Risk Analysis
Salesi M, Rostami Z, Rahimi Foroushani A, Mehrazmay AR, Mohammadi J, et al. Chronic Graft Loss and Death in Patients With Post-Transplant Malignancy in Living Kidney Transplantation: A Competing Risk Analysis. Nephro-Urol Mon. 2014;6(2):e14302. doi: https://doi.org/10.5812/numonthly.14302
Patient Survival in Renal Allograft Failure: A Time-dependent Analysis
Mirzaee M, Azmandian J, Zeraati H, Mahmoodi M, Mohammad K, et al. Patient Survival in Renal Allograft Failure: A Time-dependent Analysis. Nephro-Urol Mon. 2014;6(1):e13589. doi: https://doi.org/10.5812/numonthly.13589
- Scopus by DOI: 0
Last Update: 1 month ago
- Scopus by Title: 3
Last Update: 1 month ago
- Scopus by Title (Ref): 3
Last Update: 1 month ago
- CrossRef: 0
Last Update: 4 days ago
Ordering Reprints
Articles are published under the Creative Commons license stated on each article. No permission or royalty fee is required for uses permitted by that license. CCC handles optional bulk and customized reprint orders. Any quotation covers production and delivery services only, not copyright permission. > Request Reprints from CCC
Author(s):