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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 kidney disease (ESKD). 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...
18
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

20
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...
20
Chronic Kidney Disease IV: Nursing Management01:18

Chronic Kidney Disease IV: Nursing Management

9
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...
9
Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

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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...
12
Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

102
Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
102

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Updated: Jul 13, 2025

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Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning.

Yogita Dubey1, Pranav Mange1, Yash Barapatre1

  • 1Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India.

Diagnostics (Basel, Switzerland)
|October 14, 2023
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Summary

Machine learning models effectively detect and predict chronic kidney disease (CKD). Key predictors like serum creatinine, blood pressure, and age improve early diagnosis and patient outcomes.

Keywords:
K-nearest neighbors (KNN)XGBoost (XGB)chronic kidney disease (CKD)decision tree (DT)gradient boost (GB)histogram boost (HB)machine learning (ML)prognosisrandom forest (RF)

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Data Science

Background:

  • Chronic kidney disease (CKD) presents a substantial global health burden, necessitating advanced diagnostic and prognostic tools.
  • Early detection and accurate prognosis are critical for effective CKD management and improving patient outcomes.
  • Machine learning (ML) offers a promising avenue to enhance the identification and prediction of CKD.

Purpose of the Study:

  • To explore the efficacy of various ML algorithms in detecting and predicting CKD.
  • To improve early CKD detection and prognosis, thereby enhancing patient outcomes and reducing healthcare system strain.
  • To identify key clinical and demographic features that are most influential in CKD prediction.

Main Methods:

  • Utilized diverse ML algorithms: Gradient Boost (GB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Histogram Boost (HB), and XGBoost (XGB).
  • Evaluated algorithm performance using metrics such as accuracy, precision, recall, and F1 score.
  • Conducted feature significance analysis to pinpoint critical predictors for CKD detection and prognosis.

Main Results:

  • ML algorithms demonstrated effectiveness in detecting and predicting CKD.
  • Serum creatinine level, blood pressure, and age were identified as consistently significant predictors across models.
  • Feature analysis confirmed the pivotal role of these attributes in early CKD detection and prognosis.

Conclusions:

  • Diverse ML algorithms are effective tools for CKD detection and prediction.
  • Serum creatinine, blood pressure, and age are crucial indicators for early CKD identification and prognosis.
  • Leveraging ML enhances diagnostic accuracy and efficiency, leading to improved patient outcomes and healthcare effectiveness.