Smart Healthcare IoT: Deep Learning-Driven Patient Monitoring and Diagnosis

In this paper, we introduce an Internet of Things (IoT)-based framework customized for use in home clinical settings. This system is designed to remotely monitor patients and detect potential health issues at an early stage. The system's three primary sensors are the MAX30102 for oxygen saturation and heart rate monitoring, the AD8232 ECG sensor module for capturing ECG signal data, and the Non-contact infrared thermometry (NCIT) for precise temperature readings. Individuals' vital health data can be gathered by these devices in unison. After collection, the Message Queuing Telemetry Transport protocol is used to safely and effectively send the data to a centralized server. The categorization of possible illnesses and health abnormalities is performed on the server side using a pre-trained deep learning model built on Artificial Neural Networks (ANN). This model leverages the rich and diverse dataset collected from the sensors to identify early warning signs and detect emerging health issues. By combining IoT technology, advanced sensor capabilities, and artificial intelligence (AI), this system presents a comprehensive solution for proactive healthcare management and early intervention. The Proposed model shines with an impressive accuracy rate of 97.81%, indicating its exceptional ability to accurately forecast outcomes for nearly 98% of the cases.