Reinforcement Learning-Enhanced Dynamic LSTM Training With Meta-Feature Extraction for Small Time-Series VOC Datasets in CKD Detection

This research proposes an AI-based system that uses deep learning and reinforcement learning (RL, a machine learning approach where an AI learns by receiving rewards for good decisions) to detect disease markers in exhaled breath by analyzing volatile organic compounds (VOCs, small carbon-based chemicals produced by the body). The system is designed to work well even with small datasets and aims to improve early disease detection, particularly for chronic kidney disease, through a noninvasive and cost-effective diagnostic method.