REAL-TIME PATIENT MONITORING AND PREDICTIVE ANALYTICS WITH IOT AND DATABASES

The Internet of Things (IoT) integration into healthcare has transformed patient monitoring through the capacity to capture, send,and analyze real-time information. IoT-compliant medical equipment like wearable sensors and remote monitoring stations collectpatient health data in large quantities, which are downloaded into big databases to be analyzed further. Machine learningalgorithms analyze the data to identify future patterns of disease, forecast health risk, and suggest interventions in good time.Apart from this, SQL-based data warehouses make data management with ease, enabling quick access to, analysis, andpresentation of patient data in real-time by medical professionals. Predictive analytics, if applied, helps in effective resourceallocation, enhancing organizational efficiency, and tailoring treatment procedures. Analysis of bulk-volume data helps in theearly detection of chronic disease, minimizing hospitalization and healthcare costs. This article investigates the IoT-aidedhealthcare system design, the contribution predictive modeling makes in disease detection, and how insights based on AIinfluence better patient care and clinical decision-making. The results explain how IoT, machine learning, and SQL dataprocessing improve healthcare efficiency and enhance preventative medical interventions.