Classification of Chronic Kidney Disease in Adults Using Enhanced Recurrent Neural Networks

Abstract

Chronic kidney disease (CKD) is a prevalent health condition affecting a substantial number of adults globally. Early and accurate diagnosis of CKD is crucial for effective treatment and management. This study proposes a novel approach for the classification of CKD in adults using enhanced recurrent neural networks (RNNs). By incorporating advanced architectural enhancements and training techniques, the proposed model aims to improve the accuracy and interpretability of CKD classification. The methodology begins with the collection of relevant clinical and laboratory data from diverse sources, followed by preprocessing steps to handle missing values, normalize features, and remove noise or outliers. Important features related to CKD are then engineered from the preprocessed data using techniques such as time-series analysis or feature selection. The core of the proposed methodology lies in the design of an enhanced RNN architecture. This architecture incorporates advanced features, including long short-term memory (LSTM) cells, attention mechanisms, and residual connections. By leveraging these enhancements, the model aims to capture temporal dependencies, highlight salient information, and facilitate effective information flow, ultimately improving the overall performance. The enhanced RNN model is trained using an optimization algorithm: Adam optimizer, with appropriate hyperparameter tuning. Cross-validation techniques and statistical tests are employed to assess the significance of results. The results of the proposed methodology are expected to demonstrate improved classification accuracy and interpretability compared to traditional RNN models. The enhanced RNN model holds the potential to aid healthcare professionals in the early detection and management of CKD, leading to improved patient outcomes and reduced healthcare burden. Further research and validation on diverse datasets are necessary to establish the generalizability and effectiveness of the enhanced RNN model in real-world clinical settings.