Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models Article Swipe
Chaity Mondol
,
F. M. Javed Mehedi Shamrat
,
Md. Robiul Hasan
,
Saidul Alam
,
Pronab Ghosh
,
Zarrin Tasnim
,
Kawsar Ahmed
,
Francis M. Bui
,
Sobhy M. Ibrahim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/a15090308
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/a15090308
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/a15090308
- https://www.mdpi.com/1999-4893/15/9/308/pdf?version=1661748839
- OA Status
- gold
- Cited By
- 34
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293420779
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293420779Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/a15090308Digital Object Identifier
- Title
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Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-29Full publication date if available
- Authors
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Chaity Mondol, F. M. Javed Mehedi Shamrat, Md. Robiul Hasan, Saidul Alam, Pronab Ghosh, Zarrin Tasnim, Kawsar Ahmed, Francis M. Bui, Sobhy M. IbrahimList of authors in order
- Landing page
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https://doi.org/10.3390/a15090308Publisher landing page
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https://www.mdpi.com/1999-4893/15/9/308/pdf?version=1661748839Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1999-4893/15/9/308/pdf?version=1661748839Direct OA link when available
- Concepts
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Kidney disease, Computer science, Survivability, Artificial intelligence, Artificial neural network, Predictive modelling, Machine learning, Binary classification, Deep learning, F1 score, Medicine, Internal medicine, Support vector machine, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 12, 2023: 8Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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