Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea

Junhyug Noh, Kyung Don Yoo, Wonho Bae, Jong Soo Lee, Kangil Kim, Jang Hee Cho, Hajeong Lee, Dong Ki Kim, Chun Soo Lim, Shin Wook Kang, Yong Lim Kim, Yon Su Kim, Gunhee Kim, Jung Pyo Lee

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12 Citations (Scopus)


Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.

Original languageEnglish
Article number7470
JournalScientific reports
Issue number1
Publication statusPublished - 2020 Dec 1

Bibliographical note

Funding Information:
We express our huge gratitude to all of the Clinical Research Center for End-Stage Renal Disease investigators. This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HC15C1129 and HI15C0001).

Publisher Copyright:
© 2020, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


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