International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 18, No. 1, 8 Feb. 2026
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Diabetic Kidney Disease Prediction Using Hybrid Deep Learning Model
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Author(s)
Index Terms
Diabetic Kidney Disease (DKD), Deep Learning (DL), Convolutional Neural Network (CNN)
Abstract
Diabetic Kidney Disease (DKD) was recently identified as a significant microvascular consequence of diabetes. Many researchers are working on the classification of DKD from non-diabetic kidney disease (NDKD), but the required accuracy has not been achieved yet. This study aims to enhance diagnostic accuracy using a hybrid Deep Learning (DL) method, Convolutional Neural Network, and Long Short-Term Memory (CNN-LSTM). Clinical data on DKD were collected and preprocessed to address issues like missing values, duplicates, and outliers. Key preprocessing steps included imputation, z-score, min-max normalization, and feature encoding. Feature selection based on a correlation matrix identified the most relevant variables. Subsequently, both CNN-LSTM and Convolutional Neural Network (CNN) models were trained using processed data, with identical hyperparameters, as detailed in the methodology. Evaluation metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-score, and ROC plots were employed to assess model performance. The CNN-LSTM model achieved a high Accuracy of 98%, surpassing the CNN model’s Accuracy of 96.5%. In addition to accuracy, all metrics showed that the CNN-LSTM outperformed the CNN.
Cite This Paper
Konne Madhavi, Harwant Singh Arri, "Diabetic Kidney Disease Prediction Using Hybrid Deep Learning Model", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.18, No.1, pp. 150-162, 2026. DOI:10.5815/ijigsp.2026.01.09
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