ArticleScientific reports2023
Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 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
- Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy.Biosensors · 2026Article
- Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin.NPJ digital medicine · 2026Article
- Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.Intensive care medicine experimental · 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
- Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.BMC medical informatics and decision making · 2026Article
- A predictive model for differentiating PFA and PFB subtypes of posterior fossa ependymoma using multi-sequence MRI radiomics: a two-center study.Neuroradiology · 2026Article
- Article
- Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology.Scientific reports · 2025Article
- Machine learning prediction model for medical environment comfort based on SHAP and LIME interpretability analysis.Scientific reports · 2025Article
- Explainable prediction of hypothermia risk in laparoscopic surgery: a retrospective cross-sectional study using machine learning.BMC surgery · 2025Article
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting the risk of threatened abortion using machine learning methods: a comparative study.BMC pregnancy and childbirth · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS.Scientific reports · 2025Article
- Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.NPJ systems biology and applications · 2025Article
- Physiological, Psychological, and Functional Health Determinants of Depressive Symptoms Among the Elderly in India: Evaluation of Classification Performance of XGBoost Models.Indian journal of psychological medicine · 2025Article
- An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.Archives of academic emergency medicine · 2025Article
- Uncovering dormancy stage predictors in sweet cherry through DNA methylation and machine learning integration.Frontiers in plant science · 2025Article
- Machine learning-based identification of key biotic and abiotic drivers of mineral weathering rate in a complex enhanced weathering experiment.Open research Europe · 2025Article
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4 authors.
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Abstract
Chronic kidney disease (CKD) is a condition distinguished by structural and functional changes to the kidney over time. Studies show that 10% of adults worldwide are affected by some kind of CKD, resulting in 1.2 million deaths. Recently, CKD has emerged as a leading cause of mortality worldwide, making it necessary to develop a Computer-Aided Diagnostic (CAD) system to diagnose CKD automatically. Machine Learning (ML) based CAD system can be used by a clinician to automatically diagnoses mass people. Since ML models are considered a black box, it is also necessary to expose influential causes behind a model's prediction of a particular output. So that, a doctor can make a more rational decision based on the model's output and analysis of the features influence on the model. In this paper, we have used the XGBoost as the ML classifier to predict whether a patient has CKD or not. Using the XGBoost classifier, we have obtained an accuracy, precision, recall, and F1 score of [Formula: see text] and [Formula: see text] respectively using all [Formula: see text] features. Furthermore, we have used Biogeography Based Optimization (BBO) algorithm to find an effective subset of the features. The BBO algorithm selected almost half of the initial features. We have obtained an accuracy, precision, recall, and F1 score of [Formula: see text] and [Formula: see text] respectively using only 13 features selected by the BBO algorithm. Finally, we have explained the impact of the feature on the ML models using the SHapley Additive exPlanations (SHAP) analysis. Using SHAP analysis and BBO algorithm, we have found that hemoglobin and albumin mostly contribute to the detection of CKD.
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