Deep Learning and Optimization Approaches in Smart Healthcare Patient Monitoring System for IoT-Based Healthcare System Using Enhanced Residual Multi-Scale Diverged Self-Attention Network: A Review

Abstract

Smart healthcare systems have rapidly evolved with the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, enabling real-time patient monitoring, early disease detection, and personalized treatment. IoT-based healthcare systems collect continuous physiological data such as heart rate, oxygen saturation, ECG signals, and body temperature using wearable and remote sensing devices. However, the complexity and high dimensionality of such data require advanced analytical models for accurate interpretation. This paper presents a comprehensive review of deep learning and optimization approaches in IoT-based patient monitoring systems, focusing on advanced architectures such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Transformer models, and self-attention-based frameworks. Special emphasis is placed on enhanced residual multi-scale diverged self-attention networks, which improve feature extraction, representation, and prediction accuracy. Recent studies demonstrate that hybrid deep learning models such as CNN-LSTM and attention-based architectures significantly improve the detection of abnormalities in physiological signals. IoT-enabled systems allow real-time monitoring and remote diagnosis, reducing hospital visits and enabling proactive healthcare management.  Despite these advancements, challenges such as data heterogeneity, privacy concerns, computational complexity, and energy efficiency persist. Emerging techniques such as edge computing, federated learning, and explainable AI provide promising solutions. This review highlights recent trends (2020–2023), comparative insights, and challenges in AI-driven smart healthcare monitoring systems.