Intelligent and Optimized Deep Learning Techniques for Analyzing the IoMT-Based Remote Patient Monitoring Systems

The IoMT has dramatically empowered the process of remote patient monitoring (RPM) through much better real-time data collection and health analysis. The real struggle lies in categorizing and evaluating the patient’s health-related datas due to its complexity and unbalanced nature. This study proposes an Intelligent W-DQN model based on a whale-optimized Deep-Q-Network (W-DQN) to identify and categorize pathological information, an LSTM-QDA model for recognizing temporal patterns and reducing dimensions, and a PathoDimClassifier for selecting features to address imbalanced data and noise issues effectively. Optimization techniques, including the Whale Optimization Algorithm and the Adam optimizer, are applied to improve accuracy rates while decreasing training duration and minimizing errors. The approach seeks to enhance health monitoring in time by providing accurate categorization and prompt medical interventions to ultimately enhance patient care results.