<![CDATA[Chronic kidney disease (CKD) often remains undetected until advanced stages, limiting opportunities for timely intervention and increasing the burden of complications and treatment. This study proposes a data-driven hybrid ResNet50-SVM framework for the early diagnosis and risk stratification of CKD. Clinical data were prepared through missing-value treatment, data cleaning, Min-Max normalization, reduction of redundant information, and optimized feature selection. The processed dataset was divided into training and testing subsets using an 80:20 ratio. ResNet50 was employed to extract high-level representations, while a support vector machine performed the final classification. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion-matrix analysis, and training and validation behavior. The proposed framework achieved an accuracy of 98.76%, precision of 97.65%, recall of 99.21%, and F1-score of 98.34%. It also outperformed logistic regression, a convolutional neural network, and a decision tree across the reported evaluation metrics. The high recall indicates a strong capacity to identify patients with disease while reducing missed positive cases. These findings demonstrate the potential of hybrid deep-learning and machine-learning methods to support accurate, scalable, and timely CKD screening. Further validation using larger, more diverse clinical datasets and explainable AI methods is needed before deployment in real-world clinical decision-support systems.]]>
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