Enhancing Chronic Kidney Disease Prediction with Deep Separable Convolutional Neural Networks

Chronic Kidney Disease (CKD) is a chronic disease that progressively impairs kidney function to the point of wasting filtration, electrolyte imbalance, and blood pressure control. Early and precise prediction becomes necessary for successful disease management. This research demonstrates a new method involving Deep Separable Convolutional Neural Networks (DS-CNNs) in improving CKD prediction. Based on the Chronic Kidney Disease Dataset available at Kaggle, the model employs DS-CNNs combined with optimized techniques of optimization for better predictive accuracy. DS-CNNs utilize depthwise and pointwise convolutions to facilitate effective feature extraction and classification with efficient computation. To enhance model performance, the Learning Rate Warm-Up with Cosine Annealing technique is used to guarantee stable convergence and controlled rate of reduction in the learning rate. This solution remedies the inadequacies of traditional CKD detection solutions that are insensitive to early stages and entail expensive, invasive procedures. At 94.50% accuracy, the new DS-CNN model outcompetes conventional methods, featuring better prediction performance. The results demonstrate the utility of deep learning and optimization in early detection of CKD and introduce a promising tool for enhanced clinical decision-making.

@article{Ramesh2025,
  title     = {Enhancing Chronic Kidney Disease Prediction with Deep Separable Convolutional Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Janjhyam Venkata Naga Ramesh and P N S Lakshmi and Thalakola Syamsundararao and Elangovan Muniyandy and Linginedi Ushasree and Yousef A. Baker El-Ebiary and David Neels Ponkumar Devadhas},
  doi       = {10.14569/IJACSA.2025.01602100},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602100}
}