Early Prediction of Chronic Kidney Disease Using Deep Belief Network

Chronic kidney disease (CKD) is still a health concern despite advances in surgical care and treatment. CKD's growth in recent years has gained much interest from researchers around the world in developing high-performance methods for diagnosis, treatment and preventive therapy. Improved perfor...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.135542-135549
Main Authors: Elkholy, Shahinda Mohamed Mostafa, Rezk, Amira, Saleh, Ahmed Abo El Fetoh
Format: Article
Language:English
Subjects:
Algorithms
Belief networks
Categorical cross-entropy
chronic kidney disease
Classification
Classification algorithms
Decision making
deep belief network
Deep learning
Diseases
Entropy (Information theory)
Health services
Kidney
Kidney diseases
Machine learning
Medical diagnostic imaging
Prediction algorithms
Prediction models
Predictive models
softmax
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Summary:Chronic kidney disease (CKD) is still a health concern despite advances in surgical care and treatment. CKD's growth in recent years has gained much interest from researchers around the world in developing high-performance methods for diagnosis, treatment and preventive therapy. Improved performance can be accomplished by learning the features that are in the concern of the problem. In addition to the clinical examination, analysis of the medical data for the patients can help the health care partners to predict the disease in early stage. Although there are many tries to build intelligent systems to predict the CKD by analysis the health data, the performance of these systems still need enhancement. This Paper proposes an intelligent classification and prediction model. It uses modified Deep Belief Network (DBN) as classification algorithm to predict the kidney related diseases and the Softmax as activation function and the Categorical Cross-entropy as a loss function. The evaluation of the proposed model shows that the model can predict the CKD with accuracy 98.5% and sensitivity 87.5 % comparing with existing models. Result analysis proves that using advanced deep learning techniques is beneficial for clinical decision making and can aid in early prediction of CKD and its related phases that reduces the progression of the kidney damage.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3114306