Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

283
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...
283
Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

153
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...
153
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

37
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.
37
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

102
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...
102
Chronic Kidney Disease IV: Nursing Management01:18

Chronic Kidney Disease IV: Nursing Management

99
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...
99
Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

190
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...
190

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

BBB-aware stimuli-responsive and biomimetic nanomedicines for glioblastoma.

Chinese neurosurgical journal·2026
Same author

Structure-Based Virtual Screening of Natural Product-Derived Inhibitors Targeting Rv3806c in the Decaprenylphosphoryl-d-Arabinose Biosynthetic Pathway of <i>Mycobacterium tuberculosis</i>.

International journal of molecular sciences·2026
Same author

Association of PARP1 SNP (rs1136410) with Brain Tumor Risk: Insights from Khyber Pakhtunkhwa.

Asian Pacific journal of cancer prevention : APJCP·2026
Same author

Cardiac Dysphagia: When the Heart Disrupts Swallowing.

Cureus·2026
Same author

Clinical Utility of the Triglyceride-Glucose Index in Identifying Poor Glycemic Control in Type 2 Diabetes Mellitus: A Cost-Effective Audit.

Cureus·2026
Same author

Phytohormones as key regulators of plant resilience under salinity and extreme temperatures.

Planta·2026
Same journal

Association between food group intakes and metabolic acidosis in patients with non-dialysis-dependent chronic kidney disease.

BMC nephrology·2026
Same journal

Chronic kidney disease and reduced renal COX2 expression in xanthinuria: a case-control study.

BMC nephrology·2026
Same journal

YS-1301 ameliorates crescentic glomerulonephritis by promoting immunoregulatory macrophages.

BMC nephrology·2026
Same journal

Multiparametric functional MRI for detection of early renal allograft dysfunction after kidney transplantation: a systematic review and meta-analysis.

BMC nephrology·2026
Same journal

Association of the ratio of non-HDL-cholesterol to HDL-cholesterol with clinical and pathological characteristics in diabetic kidney disease.

BMC nephrology·2026
Same journal

How pre-education affects patients choosing appropriate renal replacement treatment: a retrospective study.

BMC nephrology·2026
See all related articles

Related Experiment Video

Updated: Oct 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Chronic kidney disease diagnosis using decision tree algorithms.

Hamida Ilyas1,2, Sajid Ali1,2,3, Mahvish Ponum4

  • 1School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.

BMC Nephrology
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study used machine learning to predict Chronic Kidney Disease (CKD) stages. The J48 algorithm achieved 85.5% accuracy, outperforming Random Forest for early CKD detection.

Keywords:
CKDDecision treeGFRJ48Machine learningRandom Forest

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Related Experiment Videos

Last Updated: Oct 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Area of Science:

  • Nephrology
  • Medical Informatics
  • Machine Learning

Background:

  • Chronic Kidney Disease (CKD) is a progressive, asymptomatic decline in renal function over months to years.
  • CKD is staged based on Glomerular Filtration Rate (GFR), influenced by factors like age, sex, race, and serum creatinine.
  • The CKD-EPI linear model is efficient for GFR estimation and CKD stage detection.

Purpose of the Study:

  • To predict various stages of Chronic Kidney Disease (CKD) using machine learning classification algorithms.
  • To develop a sustainable and practicable model for detecting CKD stages with high medical accuracy.
  • To leverage machine learning for early symptom detection and diagnosis of CKD.

Main Methods:

  • Utilized machine learning classification algorithms, specifically Random Forest and J48.
  • Applied algorithms to a dataset derived from medical records of individuals with CKD.
  • Compared the performance of J48 and Random Forest algorithms for CKD stage prediction.

Main Results:

  • The J48 algorithm demonstrated superior performance in predicting all stages of CKD compared to Random Forest.
  • J48 achieved an accuracy of 85.5% in detecting CKD stages.
  • Comparative analysis confirmed J48's improved performance over the Random Forest algorithm.

Conclusions:

  • The J48 algorithm shows significant potential for accurate CKD stage detection.
  • An automated system for detecting CKD severity can be developed using these machine learning models.
  • Early and accurate detection of CKD stages is crucial for preventing adverse health outcomes.