scholarly journals An artificial neural network‐based model to predict chronic kidney disease in aged cats

2020 ◽  
Vol 34 (5) ◽  
pp. 1920-1931
Author(s):  
Vincent Biourge ◽  
Sebastien Delmotte ◽  
Alexandre Feugier ◽  
Richard Bradley ◽  
Molly McAllister ◽  
...  
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Artificial Neural

scholarly journals Comparative Performance of Urine and Blood-based Attributes in Chronic Kidney Disease Prediction using Artificial Neural Network

2020 ◽  
Vol 17 (DEC 2020) ◽  
pp. 17-28
Author(s):  
M. H. A. Ghafar ◽  
Abdul Hadi A. Razak ◽  
M. S. A. Megat Ali ◽  
S. A. M. Al Junid ◽  
Adizul Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Disease Prediction ◽  
Comparative Performance ◽  
Artificial Neural

scholarly journals Classification of Chronic Kidney Disease in Sonography Using the GLCM and Artificial Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 864
Author(s):  
Dong-Hyun Kim ◽  
Soo-Young Ye
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Test Method ◽  
Misclassification Rate ◽  
Ultrasound Images ◽  
Ann Model ◽  
Classification Rate ◽  
Artificial Neural

Chronic kidney disease (CKD) can be treated if it is detected early, but as the disease progresses, recovery becomes impossible. Eventually, renal replacement therapy such as transplantation or dialysis is necessary. Ultrasound is a test method with which to diagnose kidney cancer, inflammatory disease, nodular disease, chronic kidney disease, etc. It is used to determine the degree of inflammation using information such as the kidney size and internal echo characteristics. The degree of the progression of chronic kidney disease in the current clinical trial is based on the value of the glomerular filtration rate. However, changes in the degree of inflammation and disease can even be observed with ultrasound. In this study, from a total of 741 images, 251 normal kidney images, 328 mild and moderate CKD images, and 162 severe CKD images were tested. In order to diagnose CKD in clinical practice, three ROIs were set: the cortex of the kidney, the boundary between the cortex and medulla, and the medulla, which are areas examined to obtain information from ultrasound images. Parameters were extracted from each ROI using the GLCM algorithm, which is widely used in ultrasound image analysis. When each parameter was extracted from the three areas, a total of 57 GLCM parameters were extracted. Finally, a total of 58 parameters were used by adding information on the size of the kidney, which is important for the diagnosis of chronic kidney disease. The artificial neural network (ANN) was composed of 58 input parameters, 10 hidden layers, and 3 output layers (normal, mild and moderate CKD, and severe CKD). Using the ANN model, the final classification rate was 95.4%, the epoch needed for training was 38 times, and the misclassification rate was 4.6%.

scholarly journals Chronic Kidney Disease Diagnosis System using Sequential Backward Feature Selection and Artificial Neural Network

2021 ◽  
Vol 317 ◽  
pp. 05030
Author(s):  
Siti Noor Chotimah ◽  
Budi Warsito ◽  
Bayu Surarso
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Kidney Disease ◽  
Early Stage ◽  
Final Diagnosis ◽  
Disease Diagnosis ◽  
Study Results ◽  
Artificial Neural

The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.

scholarly journals Chronic Kidney Disease Risk Estimation Using Artificial Neural Network

2019 ◽  
Vol 1 ◽  
pp. 4-13
Author(s):  
W. Al-Amoodi ◽  
E. Supriyanto ◽  
M. N. M. Warid
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Disease Risk ◽  
Risk Estimation ◽  
Chronic Kidney Disease Risk ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Chronic kidney disease (CKD) is the most common disease of the urinary system that can threaten the survival of the human body. Early detection and lifestyle changes can prevent kidney failure and improve the chance of survival. In West Malaysia, the prevalence of chronic kidney disease is estimated to be 9% of the population. However, screening for chronic kidney disease is still neglected at the early stages. Many equations for risk estimation of kidney failure have been developed. Some of the limitations of these equations are that they may require many laboratory tests, static and not updated. In this study, a new risk estimation model for kidney disease is developed. The risk factors of kidney disease are first identified according to their energy levels, which are Low,Medium and High. The new equation is then developed based on the relationship and the estimated weight of these risk factors. Artificial Neural Network (ANN) is utilized in this study as an alternative to classic risk equations. The MATLAB software is used to train the neural network. Retrospective data from 20 subjects are used to compare the output for the conventional equation and ANN. Another 20 samples have also been generated and compared with “Kidney Disease: Improving Global Outcomes” (KDIGO) 2012 clinical guideline heat map. The results show a slight difference between the methods. The conventional method shows its capability to estimate the risk. The result also shows the potential of the artificial neural network (ANN) to improve the accuracy of chronic kidney disease risk estimation.

Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images

Measurement ◽  
2020 ◽  
Vol 149 ◽  
pp. 106952 ◽  
Author(s):  
A. Nithya ◽  
Ahilan Appathurai ◽  
N. Venkatadri ◽  
D.R. Ramji ◽  
C. Anna Palagan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Disease Detection ◽  
Ultrasound Images ◽  
Artificial Neural

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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