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. The prevalence of dialysis was calculated as 296 per million people (pmp), and that of renal replacement therapy (RRT) was assumed to be 709 pmp worldwide in 2010 1 ; moreover, the incidence of end-stage renal disease (ESRD) is increasing steadily. Peritoneal dialysis (PD), a well-established RRT modality for patients with ESRD, varies greatly from country to country 2. The prevalence of PD has been influenced by national policies of reimbursement, and there are differences in prevalence rates between countries 3. Despite its clinical advantages, PD has been declining globally 3 , including in the Republic of Korea, where the proportion of PD decreased from 15% in 1990 to 7% in 2016 4. PD versus hemodialysis (HD) has traditionally been of major benefit regarding residual renal function 5-7 , and recent studies showed that cognitive dysfunction improved relative to HD 8. In addition, recent studies attempted remote monitoring with automated PD for home dialysis 9. Even though it has many advantages, PD use has been limited in elderly debilitated patients and in those with comorbid diseases. To achieve the benefits of PD, it is necessary to accurately upgrade predictive risk factors for hard outcomes in PD patients.