Cloud-Enabled Autoencoder with RNN Framework for Early Detection of Chronic Kidney Disease
Keywords:
Chronic Kidney Disease (CKD), Autoencoder, Recurrent Neural Networks (RNN), Cloud Computing, Machine Learning in Healthcare, Early Detection SystemsAbstract
Prevention of any progression in CKD to an advanced stage necessitates the early diagnosis of the disease. This work presents the Cloud-Enabled Autoencoder with RNN Framework for Early Detection of Chronic Kidney Disease, which exploits the combined strengths of Autoencoders for feature extraction and Recurrent Neural Networks for temporal dependencies from clinical study data. This model uses historical medical data for the prediction of CKD onset, thereby instilling the notion of proactive healthcare. The framework is hosted on a cloud platform so that the scalability and accessibility of advanced functionalities are secured for healthcare professionals. Consequently, this model enhances CKD detection and timely intervention through the integration of Autoencoders for dimensionality reduction and RNNs for sequential pattern recognition, thus improving the patient's quality of care.
