Abstract

Peripheral Artery Disease (PAD) and chronic kidney disease (CKD) are highly correlated conditions of the vascular and metabolic systems, which cause high rates of morbidity and mortality across the globe. The identification of the early development of PAD to CKD is still a clinically complicated issue with complicated multimodal patterns of data and a late onset of symptoms. The proposed study is a multi-branch hybrid deep neural network that combines Xception, VGG19, and Efficient Net to predict chronic progression of PAD to CKD based on multimodal medical images and clinical data (CT imaging and MIMIC-III EHR dataset). These complementary CNN models can be fused to learn features, which can be further used to predict and learn features, and thus provide strong representation learning. Experimental analysis shows that the proposed model attains an accuracy of 99.55, precision of 99.65, recall of 99.50, and F1-score of 99.60, which is higher than individual CNN architectures and traditional deep learning models in various feature-selection strategies and validation splits. The framework is also computationally efficient; thus, it can be deployed in resource-constrained healthcare settings. Along with the high predictive accuracy of PAD to CKD development, the suggested multi-branch hybrid deep neural network (DNN) model is also concerned with the safe treatment of patient data. Secure data transmission protocols, encryption, and access control mechanisms are employed to guarantee the confidentiality and integrity of sensitive medical information. These security measures will comply with healthcare data protection requirements and adhere to privacy laws such as HIPAA. These findings substantiate the power of hybrid feature fusion and multi-branch learning in predicting early disease progression as a reliable and scalable AI-aided decision-support system to support clinical risk stratification and patient management.