Volume 22, Issue 4 (Scientific Journal of Hamadan University of Medical Sciences-Winter 2016)                   Avicenna J Clin Med 2016, 22(4): 300-308 | Back to browse issues page

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Faradmal J, Omidi T, Pourolajal J, Roshanaei G. Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients. Avicenna J Clin Med. 2016; 22 (4) :300-308
URL: http://sjh.umsha.ac.ir/article-1-557-en.html
, gh.roshanaei@umsha.ac.ir
Abstract:   (2432 Views)

Introduction & Objective: Cox model is a common method to estimate survival and validity of the results is dependent on the proportional hazards assumption. K- Nearest neighbor is a nonparametric method for survival probability in heterogeneous communities. The purpose of this study was to compare the performance of k- nearest neighbor method (K-NN) with Cox model.

Materials & Methods: This retrospective cohort study was conducted in Hamadan Province, on 475 patients who had undergone kidney transplantation from 1994 to 2011. Data were extracted from patients’ medical records using a checklist. The duration of the  time between kidney transplantation and rejection was considered as the surviv­al time. Cox model and k- nearest neighbor method were used for Data modeling.  The prediction error Brier score was used to compare the performance models.

Results:  Out of 475 transplantations, 55 episodes of rejection occurred. 5, 10 and 15 year survival rates of transplantation were 91.70 %, 84.90% and 74.50%, respectively. The number of neighborhood optimized using cross validation method was 45. Cumulative Brier score of k-NN algorithm for t=5, 10 and 15 years were 0.003, 0.006 and 0.007, respectively. Cumulative Brier of score Cox model for t=5, 10 and 15 years were 0.036, 0.058 and 0.058, respectively.  Prediction error of k-NN algorithm for t=5, 10 and 15 years was less than Cox model that shows that the k-NN method outperforms.

Conclusions: The results of this study show that the predictions of KNN has higher accuracy than the Cox model when sample sizes and the number of predictor variables are high.

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Type of Study: Original | Subject: Special

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