Utilizing Effective Deep Learning Models for Early Prediction and Detection of Chronic Kidney Disease

Abstract

Deep learning has revolutionized disease detection and prediction by enabling highly accurate, automated analysis of complex medical data. Its ability to uncover hidden patterns allows for early and reliable diagnosis, supporting personalized treatment and improving patient outcomes. This study examines using deep learning models, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multi-layer perceptrons (MLPs), to predict chronic kidney disease (CKD) in the early stages. Since chronic kidney disease (CKD) can progress without obvious clinical symptoms, timely detection of early-stage CKD is crucial for effective treatment. A dataset from Kaggle comprising various clinical and demographic features was preprocessed for normalization and encoding, with missing values treated across both training and testing sets. The CNN model resulted in 99% accuracy, which further supports it being the best feature extractor. RNN performed with 80% accuracy in sequential data, while the MLP model gave an accuracy of 99%, which indicated that it could handle structured clinical data quite effectively. This study suggests that deep learning methodologies like CNNs have potential capabilities in the accurate prediction of CKD, which proves to be our most reliable model. Nevertheless, both RNN and MLP showed good results as well, which may indicate the robustness of models in medical diagnostics. This evidence then points toward deep learning as a solution for better screening of patients with early CKD, which could result in improved patient-centered care that is non-invasive and cost-effective. These advancements could transform CKD forecasting, enabling more tailored and preventive health interventions.