Predictive Modeling of Chronic Kidney Disease Progression Using Longitudinal Clinical Data and Deep Learning Techniques

Chronic Kidney Disease (CKD) is a prevalent condition with significant morbidity and mortality. Early and accurate detection is critical for effective treatment and management. This paper presents a comparative analysis of deep learning techniques, specifically Multi-layer Perceptron (MLP) models, for predicting CKD. We preprocessed a dataset containing clinical and demographic features, trained MLP models of varying complexities, and evaluated their performances. The results showed that simpler models achieved high accuracy, precision, recall, and F1 scores, comparable to more complex models. These findings suggest that deep learning can be a valuable tool in the early detection of CKD, with the potential to support clinical decision-making.