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In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
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Related Experiment Video

Updated: Jun 10, 2026

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.

Abdurrahim Akgundogdu¹, Serkan Kurt, Niyazi Kilic

¹Electrical-Electronics Department, Istanbul University, Avcilar, Istanbul, Turkey. akgundog@istanbul.edu.tr

Journal of Medical Systems

|August 13, 2010

Summary

This study successfully diagnosed renal failure using the Adaptive Neuro-Fuzzy Inference System (ANFIS), achieving 100% accuracy. ANFIS outperformed other methods like Support Vector Machine (SVM) and Artificial Neural Networks (ANN) for kidney disease detection.

Related Experiment Videos

Last Updated: Jun 10, 2026

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Computational Biology

Background:

Renal failure diagnosis presents challenges.
Advanced computational methods are needed for accurate disease detection.
Adaptive Neuro-Fuzzy Inference System (ANFIS) shows promise in complex pattern recognition.

Purpose of the Study:

To investigate the efficacy of ANFIS for diagnosing renal failure.
To compare ANFIS performance against Support Vector Machine (SVM) and Artificial Neural Networks (ANN).

Main Methods:

Utilized ANFIS with subtractive clustering for rule determination.
Employed Gaussian membership functions.
Input parameters included Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P), and age.

Main Results:

Achieved 100% diagnostic success rate for renal failure using ANFIS.
ANFIS demonstrated superior performance compared to SVM and ANN.
The model effectively distinguished between patients with and without renal failure.

Conclusions:

ANFIS is a highly effective tool for renal failure diagnosis.
The proposed ANFIS model offers a reliable and accurate approach to kidney disease detection.
This method holds potential for improving clinical diagnostic accuracy.