Citation :

Utkarsha Rane, Satish Ramesh Kolhe, "A Hybrid Multimodal Framework for Accurate CKD Stage Prediction via Ensemble Learning and DenseNet," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 195-206, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P116

Abstract

The gradual and irreversible condition known as Chronic Kidney Disease (CKD) that needs precise stage-wise prediction for early clinical intervention. The traditional diagnostic methods are primarily based on biochemical parameters, which may not effectively address the structural changes in the kidney status. This paper presents a multimodal machine learning framework that combines structured clinical information and kidney imaging for better chronic kidney disease stage prediction. Two different datasets were used: a hospital-retrieved tabular dataset of 500 patients with demographic and biochemical parameters, and an accessible public computed tomography dataset of 12,446 kidney pictures in the collection. Ensemble machine learning algorithms, such as Light Gradient Boosting Machine, Random Forest, and Extreme Gradient Boosting, were used for clinical dataset analysis, and a DenseNet-based convolutional neural network was used for image analysis. The Light Gradient Boosting Machine performed the best on the tabular data with an accuracy of 92.43% and a region beneath the curve of 0.988. The DenseNet model performed with 99.28% accuracy and high precision and recall values for all classes of images. The multimodal fusion approach combined the independent predictions of both datasets to improve the reliability of classification at the intermediate level. The findings show that the use of clinical and imaging modalities with separate datasets improves the accuracy of prediction and provides a reliable decision support method for evaluating chronic renal disease stages.

Keywords

Chronic Kidney Disease, Multimodal Learning, Gradient Boosting, Densenet, Medical Decision Support.

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