Diabetic Kidney Disease Prediction Using Hybrid Deep Learning Model

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.