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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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

Chronic Kidney Disease III: Interprofessional Care

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...
Diabetic Nephropathy01:28

Diabetic Nephropathy

Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...
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

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. This equation is...
Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

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

Chronic Kidney Disease IV: Nursing Management

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 fluid...

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Related Experiment Videos

Web-based Machine Learning Model for Predicting Chronic Kidney Disease in Patients with Type 2 Diabetes Mellitus: A

Lily Kresnowati1, Suhartono Suhartono2, Zahroh Shaluhiyah3

  • 1Doctoral Program of Public Health, Faculty of Public Health, Universitas Diponegoro, Semarang, Central Java, Indonesia.

F1000Research
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning tool accurately predicts chronic kidney disease (CKD) risk in type 2 diabetes patients using routine health data. This aids early detection and nephroprotective care in Indonesia.

Keywords:
chronic kidney diseasemachine learningprediction modeltype 2 diabetes mellitusweb-based calculator.

Related Experiment Videos

Area of Science:

  • Nephrology
  • Data Science
  • Public Health

Background:

  • Chronic kidney disease (CKD) is a significant complication of type 2 diabetes (T2DM), especially in resource-limited regions.
  • Early diagnosis of CKD in T2DM patients is crucial for effective nephroprotective care.
  • Predicting CKD risk from readily available clinical data can facilitate timely interventions.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting incident CKD in T2DM patients.
  • To create a web application for accessible CKD risk prediction using Indonesian national health insurance data.
  • To identify key predictors of CKD development in this population.

Main Methods:

  • Utilized BPJS Prolanis data (2017-2023) for T2DM patients without prior CKD.
  • Trained six ML algorithms (Logistic Regression, Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost) on 80% of data.
  • Validated the model on 20% of data, assessing performance with accuracy, precision, recall, F1 score, and AUC; employed SHAP for interpretability.

Main Results:

  • The CatBoost model demonstrated superior performance (AUC=0.847, accuracy=0.797).
  • Key predictors included rapid-acting insulin analogues, amlodipine, furosemide, elevated blood urea nitrogen, and folic acid.
  • Factors like advanced age and comorbidities increased risk, while certain conditions appeared protective, possibly due to healthcare utilization bias.

Conclusions:

  • A CatBoost-based web application effectively predicts incident CKD in T2DM patients using routine claims data.
  • This tool offers strong discriminative ability and supports risk stratification in primary care.
  • The application is particularly valuable for resource-limited settings like Indonesia.