Kidney Disease Prediction

Chronic Kidney Disease (CKD) is a progressive condition that can lead to kidney failure and other severe health complications if not detected early. Accurate and timely prediction of CKD is crucial for effective clinical intervention and improved patient outcomes. In this study, we propose a deep learning- based approach to predict the onset of CKD using clinical and laboratory data. A deep neural network (DNN) architecture is developed and trained on a benchmark CKD dataset, leveraging features such as blood pressure, serum creatinine, glomerular filtration rate, and other vital indicators. The model is evaluated using performance metrics including accuracy, precision, recall, and F1-score, demonstrating superior predictive capability compared to traditional machine learning methods. Our results indicate that deep learning models can serve as effective tools in clinical decision support systems, aiding in the early detection and management of CKD.