Application of Deep Learning in Identifying Novel Biomarkers for Chronic Kidney Disease Progression

Chronic kidney disease (CKD) is a major worldwide disease that requires revolutionary ways to early identification and precise prediction. The study introduces a novel Deep Learning (DL) application to solve the limitations of existing biomarker detection methods. Traditional systems often exhibit low sensitivity and specificity, making identification of CKD progression difficult. The proposed DL system makes use of advanced neural network designs to evaluate a wide range of patient data, including genetic information, clinical records, and imaging data, allowing for a thorough knowledge of disease dynamics. By automatically extracting complicated patterns and connections, the model overcomes the disadvantages of earlier methods, delivering improved accuracy in detecting possible biomarkers. The results shows that the proposed Deep Learning system for Chronic Kidney Disease (CKD) biomarker identification outperforms existing methods, achieving higher sensitivity (94.50/0), specificity (92.2 %), accuracy (90.8%), and AVC-ROC (0.94) which showcase the method as an effective tool for predicting CKD progression, highlighting its potential for early diagnosis and individualized treatment regimens. The study is an essential step in revolutionizing CKD care by incorporating cutting-edge deep-learning approaches in biomarker identification.