Deep Hybrid Framework for Early Detection of Chronic Kidney Disease Using CNN–LSTM Ensemble Models

Abstract

Chronic Kidney Disease  is a serious health issue  that often develops slowly and shows no early signs, making  timely detection very important. Regular testing and manual  diagnosis can take time and may lead to errors. This study  introduces a smart deep learning-based system that uses  Convolutional Neural Networks , Long Short-Term Memory  networks, and an Ensemble Model to predict CKD more  accurately. The CNN model helps pick up useful patterns from medical images and lab test results, while the LSTM model 
understands time-based patterns in patient records. By mixing these two types of features, the ensemble method boosts the  model’s ability to make better predictions. The system was tested on publicly available CKD datasets and outperformed regular machine learning models. The combined use of image and sequence data allows the system to learn in a more complete way, helping doctors find CKD earlier and make faster, more confident decisions. This method shows how artificial intelligence can support precision healthcare and help lower the chances of kidney failure through early and reliable prediction.