An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations
Chronic Kidney Disease (CKD) remains a pressing global public health concern, accounting for approximately 1.7 million deaths annually and disproportionately affecting aging and underserved populations. The increasing burden of CKD, particularly in low-resource settings, underscores the urgent need...
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| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Descargar el archivo Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
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| Sumario: | Chronic Kidney Disease (CKD) remains a pressing global public health concern, accounting for approximately 1.7 million deaths annually and disproportionately affecting aging and underserved populations. The increasing burden of CKD, particularly in low-resource settings, underscores the urgent need for early, accurate, and scalable diagnostic tools. This study proposes a hybrid mathematical and artificial intelligence (AI) framework for the early prediction of CKD, with a focus on supporting healthcare strategies in aging and resource-limited communities. Utilizing clinical data from a case-control study conducted in District Buner, Khyber Pakhtunkhwa, Pakistan, the framework incorporates a structured modeling pipeline that involves data preprocessing (feature extraction, missing data imputation, and categorical encoding) and class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). The proposed system integrates multiple machine learning algorithms, including logistic regression, feedforward neural networks, decision trees, support vector machines, and random forests, within a novel ensemble learning strategy designed to enhance diagnostic precision. Model robustness was assessed using three distinct train–test scenarios: (90%, 10%), (75%, 25%), and (50%, 50%). Performance evaluation employed six metrics: accuracy, specificity, sensitivity, Youden index, Brier score, and F1 score, supported by comprehensive graphical and statistical analysis. The ensemble model consistently outperformed individual classifiers, achieving a mean accuracy of 97.71%, specificity of 97.19%, sensitivity of 99.84%, Youden index of 86.55, Brier score of 1.43%, and F1 score of 98.19%. Support vector machines and random forests ranked second and third, respectively, while decision trees exhibited the lowest performance. To the best of our knowledge, this is the first ensemble-based predictive framework for CKD developed using clinical data from Pakistan. The system holds strong potential for integration into real-world biomedical decision support systems, particularly in aging and underserved populations, thereby contributing to early detection, enhanced care delivery, and optimized resource utilization in the management of chronic diseases. |
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