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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...
60
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 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...
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Dialysis01:27

Dialysis

452
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...
452
Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

70
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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Acute Kidney Injury V: Interprofessional Care01:20

Acute Kidney Injury V: Interprofessional Care

42
Acute Kidney Injury (AKI) requires a collaborative healthcare approach to restore renal function and prevent complications. Essential management strategies involve monitoring fluid and electrolyte balance, adjusting medications, initiating dialysis when necessary, and providing nutritional support.Fluid and Electrolyte ManagementFluid Monitoring: Regularly monitoring body weight, central venous pressure, and urine output helps detect fluid imbalances early. Patient intake and output are...
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A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease.

Sarah A Ebiaredoh-Mienye1, Theo G Swart1, Ebenezer Esenogho1

  • 1Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.

Bioengineering (Basel, Switzerland)
|August 25, 2022
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Summary

This study introduces a machine learning (ML) approach for early chronic kidney disease (CKD) detection. Combining feature selection with a cost-sensitive AdaBoost classifier achieved 99.8% accuracy, aiding timely clinical intervention.

Keywords:
AdaBoostchronic kidney diseasecost-sensitive learningmachine learningmedical diagnosis

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Public Health

Background:

  • Chronic kidney disease (CKD) presents a significant global health challenge with high mortality rates, particularly in developing nations.
  • Early-stage CKD often lacks discernible symptoms, hindering timely diagnosis and intervention.
  • Effective early detection is crucial for mitigating disease progression and improving patient outcomes.

Purpose of the Study:

  • To develop an efficient and cost-effective computer-aided diagnosis system for early CKD detection using machine learning.
  • To enhance the accuracy and reduce the resource requirements for CKD screening.
  • To propose a novel approach combining feature selection and a cost-sensitive classifier for improved CKD prediction.

Main Methods:

  • An information-gain-based feature selection technique was employed to identify key clinical attributes for CKD diagnosis.
  • A cost-sensitive Adaptive Boosting (AdaBoost) classifier was developed and trained on the reduced feature set.
  • The proposed method was benchmarked against existing CKD prediction techniques and other classifiers.

Main Results:

  • The proposed cost-sensitive AdaBoost model, utilizing a reduced feature set, achieved exceptional classification performance: 99.8% accuracy, 100% sensitivity, and 99.8% specificity.
  • Feature selection significantly enhanced the performance of various classifiers, demonstrating its positive impact.
  • The developed approach proved effective in CKD diagnosis and shows potential for application in other imbalanced medical datasets.

Conclusions:

  • The study successfully developed an effective predictive model for early CKD detection.
  • The integration of feature selection with a cost-sensitive AdaBoost classifier offers a promising, efficient, and accurate method for CKD screening.
  • This approach has the potential to reduce screening time and costs, and can be adapted for detecting other diseases in imbalanced datasets.