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Early Prediction of Chronic Kidney Disease

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dc.contributor.author Mondol, Chaity
dc.contributor.author Shamrat, F. M. Javed Mehedi
dc.contributor.author Hasan, Md. Robiul
dc.contributor.author Alam, Saidul
dc.contributor.author Ghosh, Pronab
dc.contributor.author Tasnim, Zarrin
dc.contributor.author Ahmed, Kawsar
dc.contributor.author Bui, Francis M.
dc.contributor.author Ibrahim, Sobhy M.
dc.date.accessioned 2023-09-24T06:37:18Z
dc.date.available 2023-09-24T06:37:18Z
dc.date.issued 22-08-29
dc.identifier.issn 1999-4893
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11111
dc.description.abstract Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Kidney disease en_US
dc.subject Machine learning en_US
dc.subject Diagnosis en_US
dc.title Early Prediction of Chronic Kidney Disease en_US
dc.title.alternative A Comprehensive Performance Analysis of Deep Learning Models en_US
dc.type Article en_US

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