Chronic Kidney Disease Detection Using Back Propagation Neural Network Classifier

Ravindra, BS; Sriraam, N.; Geetha, M.

doi:10.1109/ic3iot.2018.8668110

Cited by 11 publications

(2 citation statements)

References 15 publications

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“…Deep learning, which is central to AI learning, is a computer engineering subject related to the development of algorithms that form the basis for computer learning [ 13 , 14 ]. Work of [ 15 , 16 ] compares the performances of various machine learning algorithms, whereas [ 17 , 18 ] proposed a technique for detecting sickness in the early stages employing MATLAB by running the algorithm utilizing small quantity of learning algorithm and also added knowledge to it. References [ 19 – 21 ] used data mining techniques for the identification of chronic disorder.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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“…In human brain millions of neurons are interconnected by biological network. Similarly in ANN number of neurons are interconnected by Artificial Neural Network [1]. In deep neural network inputs are weighted based on its importance.…”

Section: Introductionmentioning

confidence: 99%

RAU: Novel Activation Function for Deep Learning Neural Network

Prajesha T M

2023

IJRITCC

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In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron activation function is responsible for generating output signals from given input signals. Hence, activation function is one of the factors that influence the performance of DNN. A novel activation unit RAU (Reciprocal activation unit) is proposed in this paper. Most of the popular algorithms given more importance to positive signals, but proposed method handles the negative and positive inputs equally. The proposed RAU tested with both multiclassification and binary classification datasets. Iris flower and Wisconsin Breast Cancer datasets are used for the analysis. In Breast cancer dataset RAU provides 99.25% and 97.08% accuracy for classification of train and test sets respectively. In Iris dataset RAU provides 99.05% and 97.78% accuracy for the classification of train and test sets. Analysis of the same datasets are performed with the existing activation functions- Sigmoid, RMAF, Swish, Tanh and ReLU. Results showed that RAU performed better than other activation functions.

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Enhancing the precision and accuracy of renal failure diagnosis using the modified support vector machine algorithm and dragonfly algorithm

2021

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Chronic Kidney Disease Detection Using Back Propagation Neural Network Classifier

Cited by 11 publications

References 15 publications

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

RAU: Novel Activation Function for Deep Learning Neural Network

Enhancing the precision and accuracy of renal failure diagnosis using the modified support vector machine algorithm and dragonfly algorithm

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