chronic kidney disease prediction or Al Driven Prediction Model for Chronic Kidney Disease Diagnosis

This research focuses on the development of an AI-driven prediction model for the diagnosis of Chronic Kidney Disease (CKD) using state-of-the-art deep learning techniques. The proposed system integrates four prominent models: Convolutional Neural Networks (CNN), MobileNet, Vision Transformer (ViT), and a hybrid of MobileNet and Long Short-Term Memory (LSTM) networks. These models are designed to effectively analyze medical images, including kidney scans, to predict the presence of CKD with high accuracy. The CNN model excels in feature extraction and hierarchical learning, MobileNet provides a lightweight architecture suitable for edge devices, while the ViT model leverages attention mechanisms for better understanding of spatial dependencies in the images. The MobileNet + LSTM hybrid combines the strengths of both image classification and sequential learning for enhanced performance in detecting temporal patterns related to CKD progression. The system is trained on a robust dataset containing various stages of CKD, enabling it to assist healthcare professionals in making early and accurate diagnoses, thus improving patient outcomes through timely intervention. This AI-based approach offers significant potential for streamlining CKD diagnosis, making it more accessible and efficient in clinical settings. Keywords: Chronic Kidney Disease, deep learning, CNN, MobileNet, Vision Transformer, LSTM, medical image analysis, early diagnosis, AI healthcare.