A MODEL FOR PREDICTING PERITONEAL DIALYSIS PATIENTS’ SURVIVAL, USING DATA MINING ALGORITHMS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

FIROUZI JAHANTIGH FARZAD; NAJAFI IRAJ; OSTOVARE MARYAM

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ) Year:2018 | Volume:75 | Issue:10 Page(s): 752-760

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

542

Download:

Cites:

Information Journal Paper

Title

A MODEL FOR PREDICTING PERITONEAL DIALYSIS PATIENTS’ SURVIVAL, USING DATA MINING ALGORITHMS

Author(s)

FIROUZI JAHANTIGH FARZAD | NAJAFI IRAJ | OSTOVARE MARYAM | Issue Writer Certificate

Keywords

CONTINUOUS AMBULATORY PERITONEAL DIALYSISQ1

DATA MININGQ1

HEALTH STATUSQ2

RETROSPECTIVE STUDIESQ1

SURVIVAL RATEQ3

Abstract

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.

Cites

No record.

References

Cite

APA: Copy

FIROUZI JAHANTIGH, FARZAD, NAJAFI, IRAJ, & OSTOVARE, MARYAM. (2018). A MODEL FOR PREDICTING PERITONEAL DIALYSIS PATIENTS’ SURVIVAL, USING DATA MINING ALGORITHMS. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), 75(10 ), 752-760. SID. https://sid.ir/paper/38485/en

Vancouver: Copy

FIROUZI JAHANTIGH FARZAD, NAJAFI IRAJ, OSTOVARE MARYAM. A MODEL FOR PREDICTING PERITONEAL DIALYSIS PATIENTS’ SURVIVAL, USING DATA MINING ALGORITHMS. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ)[Internet]. 2018;75(10 ):752-760. Available from: https://sid.ir/paper/38485/en

IEEE: Copy

FARZAD FIROUZI JAHANTIGH, IRAJ NAJAFI, and MARYAM OSTOVARE, “A MODEL FOR PREDICTING PERITONEAL DIALYSIS PATIENTS’ SURVIVAL, USING DATA MINING ALGORITHMS,” TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), vol. 75, no. 10 , pp. 752–760, 2018, [Online]. Available: https://sid.ir/paper/38485/en

Related Journal Papers