Volume 75, Issue 10 (January 2018)
Tehran Univ Med J 2018, 75(10): 752-760 |
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Firouzi Jahantigh F, Najafi I, Ostovare M. A model for predicting peritoneal dialysis patients’ survival, using data mining algorithms. Tehran Univ Med J 2018; 75 (10) :752-760
URL: http://tumj.tums.ac.ir/article-1-8478-en.html
URL: http://tumj.tums.ac.ir/article-1-8478-en.html
1- Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran. , Firouzi@eng.usb.ac.ir
2- Department of Nephrology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
3- Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran.
2- Department of Nephrology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
3- Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran.
Abstract: (4341 Views)
Background: Peritoneal dialysis is one of the most commonly used treatment methods for the patients with end stage renal failure. In recent years, the mortality rate of patients under this treatment has decreased; however, long-term survival is still an important challenge for health systems. The present study aimed to predict the survival of continuous ambulatory peritoneal dialysis patients.
Methods: In this retrospective study, according to the difference of relative importance of demographic characteristics, laboratory data, dialysis adequacy parameters and nutritional status in various patients, the factors affecting the survival of peritoneal dialysis patients have been identified by random forest algorithm. Then, the clinical and laboratory data of patients undergoing continuous ambulatory peritoneal dialysis treatment were evaluated retrospectively from July 1996 to April 2014 in 18 peritoneal dialysis centers, using multi-class one against all support vector machine (OAA-SVM) and multi-space mapped binary tree support vector machine (MBT-SVM) algorithms.
Results: 3097 patients were studied with the mean age of 50.63±15.67 years and average follow-up time of 24.48±19.13 months. The results of the random forest algorithm have identified 35 factors as the most important predictors of peritoneal dialysis patient’s survival. Then, the prediction of peritoneal dialysis patients’ survival status was evaluated using one against all support vector machine and multi-space mapped binary tree support vector machine algorithms in 5 classes of patients including “still on peritoneal dialysis”, “transferred to hemodialysis”, “received a kidney transplant”, “died” and “improved kidney function”. The reliability of survival prediction algorithms were 51.99% and 89.57% respectively.
Conclusion: An accurate prediction model would be a potentially useful way to evaluate patients’ survival at peritoneal dialysis that increased clinical scrutiny and timely intervention could be brought to bear. So, in this research, the multi-space mapped binary tree support vector machine algorithm has a high precision in predicting the survival of continuous ambulatory peritoneal dialysis patients considering multiple evaluation indices and different class distribution functions.
Methods: In this retrospective study, according to the difference of relative importance of demographic characteristics, laboratory data, dialysis adequacy parameters and nutritional status in various patients, the factors affecting the survival of peritoneal dialysis patients have been identified by random forest algorithm. Then, the clinical and laboratory data of patients undergoing continuous ambulatory peritoneal dialysis treatment were evaluated retrospectively from July 1996 to April 2014 in 18 peritoneal dialysis centers, using multi-class one against all support vector machine (OAA-SVM) and multi-space mapped binary tree support vector machine (MBT-SVM) algorithms.
Results: 3097 patients were studied with the mean age of 50.63±15.67 years and average follow-up time of 24.48±19.13 months. The results of the random forest algorithm have identified 35 factors as the most important predictors of peritoneal dialysis patient’s survival. Then, the prediction of peritoneal dialysis patients’ survival status was evaluated using one against all support vector machine and multi-space mapped binary tree support vector machine algorithms in 5 classes of patients including “still on peritoneal dialysis”, “transferred to hemodialysis”, “received a kidney transplant”, “died” and “improved kidney function”. The reliability of survival prediction algorithms were 51.99% and 89.57% respectively.
Conclusion: An accurate prediction model would be a potentially useful way to evaluate patients’ survival at peritoneal dialysis that increased clinical scrutiny and timely intervention could be brought to bear. So, in this research, the multi-space mapped binary tree support vector machine algorithm has a high precision in predicting the survival of continuous ambulatory peritoneal dialysis patients considering multiple evaluation indices and different class distribution functions.
Keywords: continuous ambulatory peritoneal dialysis, data mining, health status, retrospective studies, survival rate
Type of Study: Original Article |
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