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Related Concept Videos

Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

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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...
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Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP.

Ming-Che Tsai1,2, Bannakij Lojanapiwat3, Chi-Chang Chang4,5

  • 1Department of Emergency Medicine, School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan.

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|December 9, 2023
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Summary

A new machine learning model accurately predicts chronic kidney disease (CKD) risk in Thailand, identifying key factors like serum albumin and blood urea nitrogen to aid personalized treatment strategies.

Keywords:
SHAPThailandartificial intelligencechronic kidney diseaserandom forest

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Area of Science:

  • Nephrology
  • Data Science
  • Public Health

Background:

  • Chronic kidney disease (CKD) is a significant health burden in Thailand, affecting 17.5% of the population.
  • Advanced stages of CKD and the need for hemodialysis are substantial, highlighting the need for effective management and early detection.

Purpose of the Study:

  • To develop and validate a robust risk prediction model for chronic kidney disease (CKD) specific to the Thai population.
  • To identify critical independent variables contributing to CKD risk.

Main Methods:

  • Utilized data from 17,100 patients to screen 14 independent risk factors.
  • Employed machine learning algorithms including IBK, Random Tree, Decision Table, J48, and Random Forest, with synthetic minority oversampling technique (SMOTE) to address data imbalance.
  • Applied SHapley Additive exPlanations (SHAP) for in-depth analysis of critical risk factors.

Main Results:

  • The Random Forest model achieved a high accuracy rate of 92.1%.
  • Key predictive factors identified include serum albumin, blood urea nitrogen, age, direct bilirubin, and glucose.
  • SHAP analysis provided insights into the significance of individual and dual-attribute risk factors.

Conclusions:

  • The developed machine learning model offers a reliable tool for CKD risk prediction in Thailand.
  • The identified risk factors can inform the development of personalized treatment and prevention strategies for CKD.
  • This approach enhances the understanding of multifactorial CKD etiology and management.