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Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A
Raoof Nopour1, Mostafa Shanbehzadeh2
1Department of Health Information Management, School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran.
Health Science Reports
|April 27, 2026
Summary
This study developed an AI model using Random Forest to predict the 5-year mortality risk in chronic kidney disease (CKD) patients. The model demonstrated high accuracy, identifying key factors like age and eGFR for improved clinical decision-making.
Area of Science:
- Nephrology
- Medical Informatics
- Artificial Intelligence
Background:
- Chronic kidney disease (CKD) is a growing global health concern.
- Early prediction of CKD progression and mortality is crucial for effective management.
- Existing artificial intelligence (AI) models show promise in CKD prognosis, but 5-year mortality risk prediction requires further investigation.
Purpose of the Study:
- To develop and evaluate AI-driven prognostic models for predicting the 5-year mortality risk in hospitalized CKD patients.
- To enhance understanding of AI's capabilities in CKD prognostication.
- To optimize preventive and treatment strategies for CKD through improved risk prediction.
Main Methods:
- A retrospective study involving 1543 CKD patients from four clinical centers.
- Application of AI algorithms, specifically Random Forest (RF), to establish prognostic models.
- Analysis of demographic characteristics, comorbidities, vital signs, laboratory findings, and treatments using univariate and multivariate statistics.
- Evaluation of model performance using accuracy, calibration indicators, and SHAP analysis.
Main Results:
- The Random Forest model achieved high performance, with an accuracy of 96.36% and an AU-ROC of 0.941.
- Key predictive factors identified by SHAP analysis include age, estimated Glomerular Filtration Rate (eGFR), dialysis, mechanical ventilation, Prothrombin Time (PT), and Partial Thromboplastin Time (PTT).
- The model demonstrated strong predictive values, including a Positive Predictive Value (PPV) of 96.57% and a Negative Predictive Value (NPV) of 96.15%.
Conclusions:
- The Random Forest AI model shows significant potential for enhancing the prediction of mortality risk in CKD patients.
- This AI-driven approach can improve clinical decision-making and patient management strategies.
- Further research into AI applications can lead to more personalized and effective CKD care.
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