Deep Learning-Based Death Prediction Model for Chronic Kidney Disease

Objectives The prevalence of chronic kidney disease (CKD) continues to rise, making it one of the leading causes of death worldwide. Recent advances in medical and health research have progressed beyond traditional statistical methodologies, increasingly leveraging artificial intelligence to identify and predict factors influencing mortality. Further AI-based research is therefore essential to deepen understanding of the determinants of death among CKD patients. Methods This study used data from the Korea Disease Control and Prevention Agency’s in-depth survey of patients discharged between 2016 and 2021. Least absolute shrinkage and selection operator (LASSO) regression, a machine learning technique, was applied to identify significant factors associated with death in CKD patients. These selected variables were then incorporated into a deep learning-based predictive model. Results Eight factors influencing death were identified, including length of hospital stay (coefficient = 0.023), emergency admission (0.016), age (0.013), severity-adjusted score (0.008), and regional differences (0.003). The developed deep learning model achieved a loss value of 0.1207 and an accuracy of 96.84%. Conclusions This study identified emergency visits and prolonged hospital stays as key predictors of death in CKD patients. To mitigate these risks, regular monitoring by nephrology specialists and timely initiation of renal replacement therapy are essential. Age also emerged as a critical determinant, emphasizing the importance of age-stratified clinical guidelines amid global aging trends. The high-performing, simplified predictive model based on general characteristics offers a valuable tool for rapid prognosis assessment in primary and secondary healthcare settings.

• Publication year

  2025

• Venue

  Healthcare Informatics Research

• Publication date

  2025-10-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.4258/hir.2025.31.4.396PMID 41265425PMCID 12640731
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• CKD Progression Risk and Subsequent Cause of Death: A Population-Based Cohort Study

  2023influential reference
• Trends in the Prevalence of Chronic Kidney Disease, 2011–2021

  2023cited by this paper
• A Machine-Learning-Based Risk Factor Analysis for Hypertension: Korea National Health and Nutrition Examination Survey 2016–2019

  2022cited by this paper
• Epidemiology of chronic kidney disease: an update 2022.

  2022cited by this paper
• Korean Society of Nephrology hemodialysis unit accreditation report (2016–2020) and future directions

  2022cited by this paper
• Research on a diagnostic model of deep learning-based pneumonia using defense medical data

  2021cited by this paper
• Random Forest

  2021cited by this paper
• XGBoost Model for Chronic Kidney Disease Diagnosis

  2020cited by this paper
• Logistic LASSO Regression for Dietary Intakes and Breast Cancer

  2020influential reference
• Effect of Critical Thinking Disposition and Clinical Decision Making on Patient Safety Competence of Nurses in Hemodialysis Units

  2018cited by this paper
• Precision medicine in type 2 diabetes

  2018cited by this paper
• Chronic Kidney Disease

  2017cited by this paper
• Random Forest.

  2017cited by this paper
• Effect of Work Environment on Nursing Performance of Nurses in Hemodialysis Units: Focusing on the Effects of Job Satisfaction and Empowerment

  2016cited by this paper
• The Effects of Characteristics of Nurses on Knowledge and Nursing Performance Evaluation of Evidence Based Hemodialysis Nursing Practice in Hemodialysis Unit Nurses

  2016cited by this paper
• Body Mass Index, Mortality, and Gender Difference in Advanced Chronic Kidney Disease

  2015cited by this paper
• Chronic kidney disease: global dimension and perspectives

  2013influential reference
• Chronic kidney disease

  2012cited by this paper
• Conceptual model of CKD: applications and implications.

  2009cited by this paper
• Result and Improvement Direction of Korean Society of Nephrology Hemodialysis Unit Accreditation

  2008cited by this paper
• Using principal components for estimating logistic regression with high-dimensional multicollinear data

  2006cited by this paper
• Patients' Referral Pattern and Dialysis Initiation Practice: Single Center Experience.

  1999cited by this paper
• [Statistics on the cause of death].

  1985cited by this paper

• No citing papers are available for this paper.