Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients
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 compa...
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| Format: | Article |
| Language: | fas |
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Hamadan University of Medical Sciences
2016-03-01
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| Series: | پزشکی بالینی ابن سینا |
| Subjects: | |
| Online Access: | http://sjh.umsha.ac.ir/article-1-557-en.html |
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| author | Javad Faradmal Tahereh Omidi Jalal Pourolajal Ghodratollah Roshanaei |
| author_facet | Javad Faradmal Tahereh Omidi Jalal Pourolajal Ghodratollah Roshanaei |
| author_sort | Javad Faradmal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T15:57:40Z |
| format | Article |
| id | doaj.art-4e0fb585b59143e5836ace601aec465f |
| institution | Directory Open Access Journal |
| issn | 2588-722X 2588-7238 |
| language | fas |
| last_indexed | 2024-12-13T15:57:40Z |
| publishDate | 2016-03-01 |
| publisher | Hamadan University of Medical Sciences |
| record_format | Article |
| series | پزشکی بالینی ابن سینا |
| spelling | doaj.art-4e0fb585b59143e5836ace601aec465f2022-12-21T23:39:17ZfasHamadan University of Medical Sciencesپزشکی بالینی ابن سینا2588-722X2588-72382016-03-01224300308Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant PatientsJavad Faradmal0Tahereh Omidi1Jalal Pourolajal2Ghodratollah Roshanaei3 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.http://sjh.umsha.ac.ir/article-1-557-en.htmlcox modelkidney transplantationk- nearest neighbortransplantation rejection |
| spellingShingle | Javad Faradmal Tahereh Omidi Jalal Pourolajal Ghodratollah Roshanaei Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients پزشکی بالینی ابن سینا cox model kidney transplantation k- nearest neighbor transplantation rejection |
| title | Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients |
| title_full | Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients |
| title_fullStr | Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients |
| title_full_unstemmed | Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients |
| title_short | Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients |
| title_sort | comparison of cox model and k nearest neighbor to estimation of survival in kidney transplant patients |
| topic | cox model kidney transplantation k- nearest neighbor transplantation rejection |
| url | http://sjh.umsha.ac.ir/article-1-557-en.html |
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