An Analytical Review of IoT-Based Health Monitoring Systems Integrating CNN–LSTM for Early Disease Detection

 
The purpose of the review paper is to review the use of the IoT-based real-time health monitoring systems and CNN -LSTM deep learning models to detect diseases in their early stages. We have the intention to explore how the IoT technologies and hybrid deep-learning structures can be useful in improving the continuous patient monitoring and predictive healthcare outcomes. The study design will comprise a critical review of the literature and comparative analysis of the recent sources dedicated to the IoT-based healthcare systems, sensors and deep learning models such as CNN, LSTM, and Bi-LSTM. The literature review of the system architectures, data processing techniques, and performance measurements are analyzed. The findings demonstrate that the IoT devices can be used to obtain physiological measurements in an extremely efficient way in real time, and CNNLSTM models can extract features and recognize temporal patterns and can, therefore, be utilized to predict illnesses more efficiently as compared to traditional machine learning techniques. A number of studies document high accuracy rates with a few models recording more than 98 percent disease prediction rates. Nonetheless, other issues, including data privacy, interoperability, computational complexity, and lack of labeled datasets, are still of great concern. The review finds that incorporating IoT and hybrid deep learning models provide an exciting way to create intelligent, scalable, and patient-centered healthcare systems and that future developments will rely on edge computing, explainable AI, and secure data-sharing systems.