A Hybrid CNN–LSTM–GRU Deep Learning Model for the Accurate Classification of Chronic Kidney Disease
Received: 20 August 2025 | Revised: 31 August 2025 and 7 September 2025 | Accepted: 9 September 2025 | Online: 8 December 2025
Corresponding author: Ahmed Fahim
Abstract
Chronic Kidney Disease (CKD) is a progressive and often undiagnosed condition that poses a significant global health risk due to its silent progression and typical detection at a late stage. This study presents an advanced hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) architectures to improve the early prediction and classification of CKD. The framework employs a preprocessing pipeline that includes data cleaning, normalization, and class balancing using the Synthetic Minority Oversampling Technique (SMOTE) before performing deep feature extraction and sequence modeling. The hybrid CNN-LSTM-GRU model demonstrated outstanding performance, achieving an accuracy of 98.75%, a precision of 100%, a recall of 97.56%, an F1-score of 98.77%, and an Area Under the ROC Curve (AUC) of 0.988. These results significantly outperform conventional models such as LSTM, GRU, DNN, and 1D-CNN. The proposed framework has strong potential to support clinical decision-making systems for accurate, early CKD diagnosis, thereby improving patient outcomes and reducing the burden on healthcare systems.
Keywords:
chronic kidney disease, hybrid neural network, health informatics, CNN–LSTM–GRU, deep learningDownloads
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Copyright (c) 2025 Ahmed Fahim, Ahmed M. Osman, Zahraa Tarek, Ahmed M. Elshewey
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