AI-Powered Kidney Health: A Deep Neural Architecture for Chronic Kidney Disease Recognition and Prognosis

Abstract

Chronic Kidney Disease (CKD) is a progressive and life-threatening disorder that often culminates in end-stage renal failure, necessitating dialysis or transplantation. Early and accurate identification of CKD is therefore crucial to preventing irreversible outcomes and reducing healthcare burdens. This paper introduces an AI-powered deep neural architecture integrated with advanced ensemble classifiers for CKD recognition and prognosis. By accurately analyzing patient data, artificial intelligence is facilitating the early detection of kidney disease through sophisticated machine learning algorithms. The proposed framework employs feature engineering and deep learning to capture complex, non-linear interactions among clinical variables, while predictive modeling enhances discriminative power. From an initial set of 25 attributes, dimensionality reduction and feature selection strategies identified the most influential subset of parameters, optimizing diagnostic efficiency. Multiple state-of-the-art classifiers, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and ensemble models such as XGBoost and LightGBM, were systematically evaluated. Among these, the hybrid deep learning–XGBoost architecture achieved the highest performance, recording 98.3% accuracy, 0.98 precision, 0.98 recall, and 0.98 F1-score. The results highlight that combining deep neural architectures with advanced ensemble methods offers superior predictive capability for CKD detection and prognosis. Furthermore, the framework establishes a scalable paradigm for AI-driven healthcare applications, extending beyond kidney disease toward broader clinical decision support and precision medicine.

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