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Related Concept Videos

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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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...
161
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

40
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.
40
Acute Kidney Injury I: Introduction01:22

Acute Kidney Injury I: Introduction

209
Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
209
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

106
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...
106
Dialysis01:27

Dialysis

772
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
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Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination

Ebrahime Mohammed Senan1, Mosleh Hmoud Al-Adhaileh2, Fawaz Waselallah Alsaade3

  • 1Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India.

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Early diagnosis of chronic kidney disease (CKD) is crucial. Machine learning models, particularly random forest, achieved 100% accuracy in detecting CKD, aiding preventive measures.

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Data Science

Background:

  • Chronic kidney disease (CKD) affects 10% of adults globally and is a leading cause of death.
  • Effective early diagnosis measures for CKD are essential due to its increasing prevalence.
  • Current diagnostic methods require enhancement for timely intervention and prevention of kidney failure.

Purpose of the Study:

  • To develop a machine learning-based diagnostic system for early detection of chronic kidney disease (CKD).
  • To evaluate the performance of various machine learning algorithms in identifying CKD.
  • To assist healthcare professionals in implementing preventive strategies through early CKD diagnosis.

Main Methods:

  • A dataset of 400 patients with 24 features was analyzed.
  • Missing numerical and nominal values were imputed using mean and mode imputation.
  • Recursive Feature Elimination (RFE) was employed for feature selection.
  • Four classification algorithms were applied: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Random Forest.

Main Results:

  • All applied classification algorithms demonstrated promising performance in CKD detection.
  • The Random Forest algorithm achieved superior results, reaching 100% accuracy, precision, recall, and F1-score.
  • Feature selection using RFE identified key indicators for CKD diagnosis.

Conclusions:

  • Machine learning techniques, especially Random Forest, are highly effective for the early detection of CKD.
  • AI-powered diagnostic tools can significantly support clinicians in diagnosing CKD and preventing its progression.
  • Early diagnosis through advanced computational methods is vital for managing CKD and reducing mortality rates.