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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2026
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| author | Iftikhar, Hasnain Hashem, Atef F. Mohamud, Liban Ali Al-Moisheer, A. S. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk |
| author_browse | Al-Moisheer, A. S. Gonzales Medina, Ronny Ivan Hashem, Atef F. Iftikhar, Hasnain López Gonzales, Javier Linkolk Mohamud, Liban Ali |
| author_facet | Iftikhar, Hasnain Hashem, Atef F. Mohamud, Liban Ali Al-Moisheer, A. S. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/396 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3962026-10-30T16:14:36ZAn intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populationsIftikhar, HasnainHashem, Atef F.Mohamud, Liban AliAl-Moisheer, A. S.Gonzales Medina, Ronny IvanLópez Gonzales, Javier LinkolkChronic Kidney DiseaseMachine LearningImbalanced DataEnsemble LearningAging PopulationsSmoteEarly Disease PredictionBiomedical Decision Support SystemsSustainability Development GoalLow-Resource Healthcare SettingsClinical Data AnalyticsChronic 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.Articlehttps://pubmed.ncbi.nlm.nih.gov/41530306https://www.nature.com/articles/s41598-025-32919-6_reference.pdfhttps://doi.org/10.1038/s41598-025-32919-6PMC1282726710.1038/s41598-025-32919-6https://hdl.handle.net/20.500.15037/39641530306W7123613175enScientific Reports2045-23222026-01-132026-06-13T08:12:38Z2026-06-13T08:12:38Z |
| spellingShingle | Chronic Kidney Disease Machine Learning Imbalanced Data Ensemble Learning Aging Populations Smote Early Disease Prediction Biomedical Decision Support Systems Sustainability Development Goal Low-Resource Healthcare Settings Clinical Data Analytics Iftikhar, Hasnain Hashem, Atef F. Mohamud, Liban Ali Al-Moisheer, A. S. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title | An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title_full | An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title_fullStr | An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title_full_unstemmed | An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title_short | An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| title_sort | intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations |
| topic | Chronic Kidney Disease Machine Learning Imbalanced Data Ensemble Learning Aging Populations Smote Early Disease Prediction Biomedical Decision Support Systems Sustainability Development Goal Low-Resource Healthcare Settings Clinical Data Analytics |
| topic_facet | Chronic Kidney Disease Machine Learning Imbalanced Data Ensemble Learning Aging Populations Smote Early Disease Prediction Biomedical Decision Support Systems Sustainability Development Goal Low-Resource Healthcare Settings Clinical Data Analytics |
| url | https://pubmed.ncbi.nlm.nih.gov/41530306 https://www.nature.com/articles/s41598-025-32919-6_reference.pdf https://doi.org/10.1038/s41598-025-32919-6 https://hdl.handle.net/20.500.15037/396 |
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