ArticleHealth science reports2026
Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Recent advancements and future prospects on AI-integrated sensing techniques for non-invasive chronic kidney disease diagnosis: a review.Frontiers in artificial intelligence · 2026Review
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2 authors.
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Abstract
Background and Aims: Most countries are experiencing an increasing trend of chronic kidney disease (CKD). Preventive strategies, such as early prediction of CKD progression and mortality, are crucial in reducing this disease rate. Although artificial intelligence (AI) models have demonstrated outstanding predictive performance in the prognosis of CKD, no study has been conducted on the 5-year mortality risk of this disease. This study aims to perform this task to gain a deeper understanding of this technology and optimize preventive and treatment strategies. Methods: This retrospective study utilized 1543 CKD hospitalized patients referred to four clinical centers in Tehran City from November 2022 to December 2024. We leveraged AI algorithms to establish prognostic models for the 5-year mortality risk of CKD. The accuracy and calibration indicators were utilized to determine performance eligibility. The prognostic factors, including demographic characteristics, comorbidities, vital parameters, laboratory findings, and medical treatments, were analyzed using both univariate and multivariate statistics for this purpose. Results: Ultimately, 1528 samples were employed for model construction. Our empirical results revealed that Random Forest (RF) (Positive Predictive Value (PPV) of 96.57% and 95% CI of 95.03-98.1, Negative Predictive Value (NPV) of 96.15% and 95% CI of 94.48-98.93, sensitivity of 96.13% and 95% CI of 94.39-98.85, specificity of 96.58% and 95% CI of 94.13-98.54, accuracy of 96.36% and 95% CI of 94.47-99.01, Discussion: This study revealed that RF could potentially enhance the predictive strength of mortality in patients with CKD and decision-making in clinical environments.
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