Early detection of kidney illness can be achieved by training machine learning algorithms to discover patterns in patient data, such as imaging, test results, and medical history. This will enable rapid diagnosis and start of treatment regimens, which can improve patient outcomes. With 98.97% accuracy in CKD detection, the suggested TrioNet with KNN imputer and SMOTE fared better than other models. This comprehensive research highlights the model's potential as a useful tool in the diagnosis of chronic kidney disease (CKD) and highlights its capabilities.

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[1] Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick, "Chronic kidney disease", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/2x4h-ev87. Accessed: Sep. 17, 2024.
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doi = {10.21227/2x4h-ev87},
url = {http://dx.doi.org/10.21227/2x4h-ev87},
author = {Debosree Ghosh; Kishore Ghosh; Dipankar Das; Abdul Rafeul Mallick },
publisher = {IEEE Dataport},
title = {Chronic kidney disease},
year = {2024} }
TY - DATA
T1 - Chronic kidney disease
AU - Debosree Ghosh; Kishore Ghosh; Dipankar Das; Abdul Rafeul Mallick
PY - 2024
PB - IEEE Dataport
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ER -
Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick. (2024). Chronic kidney disease. IEEE Dataport. http://dx.doi.org/10.21227/2x4h-ev87
Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick, 2024. Chronic kidney disease. Available at: http://dx.doi.org/10.21227/2x4h-ev87.
Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick. (2024). "Chronic kidney disease." Web.
1. Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick. Chronic kidney disease [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/2x4h-ev87
Debosree Ghosh, Kishore Ghosh, Dipankar Das, Abdul Rafeul Mallick. "Chronic kidney disease." doi: 10.21227/2x4h-ev87