ArticlePloS one2023
Chronic kidney disease prediction using boosting techniques based on clinical parameters.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Bioinspired hybrid optimisation and deep belief neural networks for early chronic kidney disease detection: an explainable clinical AI framework.Renal failure · 2026Article
- Data-driven explainable chronic kidney disease detection using RF based data imputation and meta-ensemble learning.Scientific reports · 2026Article
- Early Detection of Chronic Kidney Disease in Men Using Lifestyle and Demographic Indicators: A Machine Learning Approach for Primary Healthcare Settings.Healthcare (Basel, Switzerland) · 2026Article
- Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data.PloS one · 2026Article
- Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images.Scientific reports · 2025Article
- Ensemble learning approaches for early prediction of chronic kidney disease based on polysomnographic phenotype analysis.NPJ digital medicine · 2025Article
- Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis.Scientific reports · 2025Article
- Enhanced and Interpretable Prediction of Multiple Cancer Types Using a Stacking Ensemble Approach with SHAP Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets.Scientific reports · 2025Article
- Assessment of skin fibrosis in a murine model of systemic sclerosis with multifunctional optical coherence tomography.Journal of biomedical optics · 2025Article
- Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.BMC medical informatics and decision making · 2024Article
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- Enhancing machine learning-based forecasting of chronic renal disease with explainable AI.PeerJ. Computer science · 2024Article
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Authors and funding
4 authors.
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Abstract
Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.
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