Comparing Three Data Mining Methods to Predict Kidney Transplant Survival.
BACKGROUND
One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant.


METHODS
To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used.


RESULTS
Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models.


CONCLUSIONS
Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics.
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Leila
L
Shahmoradi
Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran.
Mostafa
M
Langarizadeh
Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Gholamreza
G
Pourmand
Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Ziba Aghsaei
ZA
Fard
Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Alireza
A
Borhani
C&RTree algorithm; C5.0 algorithm; CRISP methodology; data mining; kidney transplantation; neural network algorithm; survival