Diagnosing Chronic Kidney disease using Artificial Neural Network (ANN)
Keywords:
Machine learning, Artificial Neural Network (ANN), Chronic kidney disease(CKD) , Google Colab NotebookAbstract
The prevalence of chronic kidney disease (CKD), brought on by environmental pollution and a lack
of safeguards for people's health, is rising globally. A slow and steady decrease in kidney function
over many years is chronic kidney disease (CKD). A person may eventually get renal failure. Using
artificial neural networks in concert with the machine learning techniques (ANN), Keras, and Google
Colab Notebook for serial model construction, this study intends to propose a potent method for
identifying chronic kidney disease.
This study looked into ANN's accuracy, sensitivity, and specificity in the diagnosis of CKD. Based
on the dataset's purpose, categorization of technology's effectiveness. In order to decrease the feature
dimension and increase classification system accuracy, an algorithm model including ANN has been
developed.
Results indicate that ANN architecture, which was used, achieved the best accuracy (98.56%),
whereas other methods, such as SVM, Random-forest, and K-Nearest Neighbor (KNN), delivered
accuracy levels that were lower than those of ANN.
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References
Webster et al, (2017). "Chronic kidney disease. The Lancet 389(10075):1238–1252 ". s.l.:s.n.
Vital TP, N. M. A. T. S. A., (2016). A neural network approach for classification of kidney disease dataset
collected from Visakhapatnam of A.P.". India.: s.n.
Njoud Abdullah Almansour, H. F. S. N. R. K. R. K. A., (2019). "Neural network and support vector machine
for the prediction of chronic kidney disease". Computers in Biology and Medicine.
S.Priya1, S. K. G. S. E., 2020. "CHRONIC KIDNEY DISEASE PREDICTION USING NEURAL NETWORKS".. IRJET,.
Nahua Kang, (2017). "Multi-Layer Neural Networks with Sigmoid Function— Deep Learning for Rookies
(2)".. s.l.:s.n.
Kevin L. Priddy and paul E. Keller, (2005). "ARTIFICIAL NEURAL NETWORKS INTRODUCTION".. Bellingham,
Washinton USA: s.n.
Mrs. Hemalatha.R1, R. N. ,. N., (2021). "Diagnosis of Chronic Kidney Disease Using Artificial Neural
Network". Turkish Journal of Computer and Mathematics Education.
www.mayoclinic.org/diseases-conditions/chronickidney-disease/symptoms-causes, 2., n.d.
Dayhoff JE, D. J., 2001. "Artificial neural networks: opening the black box. Cancer".
https://www.kidney.org/news/newsroom/factsheets/End-Stage-Renal-Disease-in-the-US, 4., ( 2018).
Amato F, L. A. P.-M. E. V. P. H. A. H. J. ,., (2013). "Artificial neural networks in medical diagnosis".. J Appl
Biomed,.
A. Survey, (2017). "Importance of artificial neural networks for the location of faults in transmission
systems". Surveyor, , p. 357–362..
Berina Alic, e. a., (2015). "Machine Learning Techniques for Classification of Diabetes And Cardiovascular
Diseases".
Suresh Kumar, S. P. e. a., (2018). “ Chronic Kidney Disease Prediction Using Machine".
Elhousaany Rady and Ayman Anwar et al, (2019). “Prediction of Kidney Disease Stages Using.
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