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Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

311
Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

996
Chronic Kidney Disease (CKD) progressively impairs multiple body systems due to the accumulation of uremic toxins, which disrupt cellular functions across various organs.Neurologic symptomsNeurologic symptoms often arise early in CKD, as uremic toxin buildup drives changes in cognitive and motor functions. Patients frequently experience fatigue, headache, confusion, difficulty concentrating, and, in severe cases, seizures. Peripheral neuropathy commonly manifests as burning sensations in the...
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Chronic Kidney Disease IV: Nursing Management01:18

Chronic Kidney Disease IV: Nursing Management

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Nursing management is essential for preventing complications, maintaining stability, and improving patients' quality of life in chronic kidney disease (CKD). By using a structured approach, nurses help slow CKD progression and support effective patient care​.1. Comprehensive patient assessmentEffective management begins with nurses reviewing the patient’s medical history, and identifying key risk factors like diabetes, hypertension, and nephrotoxic drug use. Nurses assess signs of...
630
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

513
Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
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Related Experiment Video

Updated: Apr 9, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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Community-based early-stage chronic kidney disease screening using explainable machine learning for low-resource

Muhammad Ashad Kabir1, Sirajam Munira2, Dewan Tasnia Azad3

  • 1School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia.

International Journal of Medical Informatics
|April 7, 2026
PubMed
Summary

This study developed an explainable machine learning framework for early chronic kidney disease (CKD) detection in low-resource settings. The model accurately identifies CKD risk using accessible, non-laboratory variables, improving community screening.

Keywords:
Chronic kidney diseaseCommunity-basedEarly stageExplainableFeature selectionMachine learningScreening

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Area of Science:

  • Medical Informatics
  • Machine Learning Applications
  • Public Health

Background:

  • Early detection of chronic kidney disease (CKD) is crucial for preventing end-stage renal disease.
  • Existing screening tools often underperform in South Asia due to differing risk profiles and reliance on high-income country data.
  • Many current machine learning (ML) models for CKD use pathology tests and hospital data, limiting community-level applicability in resource-limited settings.

Purpose of the Study:

  • To develop and evaluate an explainable ML framework for community-based early-stage CKD screening.
  • To create optimized predictor subsets using all available variables and variables excluding pathology tests for practical risk assessment.
  • To enable accurate and accessible CKD risk prediction in low-resource settings.

Main Methods:

  • A community-based CKD dataset from Bangladesh was utilized to develop predictive models.
  • Ten feature selection methods identified robust predictor subsets, and twelve ML classifiers were evaluated.
  • External validation was performed on datasets from India, UAE, and Bangladesh, with SHAP used for model interpretation.

Main Results:

  • The best ML model achieved 90.4% balanced accuracy using all variables and 89.23% using only non-pathology tests.
  • The framework outperformed existing screening tools, requiring fewer and more accessible inputs.
  • External validation showed strong generalizability with sensitivities from 78% to 98%, and SHAP identified clinically relevant predictors.

Conclusions:

  • Accurate, interpretable, and scalable early-stage CKD screening is feasible using only non-pathology-test features.
  • This ML framework shows significant potential for effective community-level CKD screening in resource-constrained environments.
  • The study highlights the importance of developing context-specific tools for global health equity in CKD management.