<![CDATA[The increasing demand for real-time, patient-centrichealthcare has accelerated the integration of Internet of Things(IoT) technologies with intelligent analytics. Traditional healthcare models rely on episodic patient visits and are inadequate forcontinuous monitoring and early detection of critical conditions.This paper presents CareMatrix, an integrated smart healthcaresystem combining IoT-based Remote Patient Monitoring (RPM)with machine learning for real-time anomaly detection.The system utilizes an ESP32 microcontroller integrated witha MAX30102 sensor to acquire physiological parameters suchas heart rate and oxygen saturation (SpO2). The data arepreprocessed at the edge and transmitted via WiFi to theThingSpeak cloud using REST APIs for real-time storage andvisualization.A Random Forest classifier trained on 10,000 patient recordsachieves an accuracy of 92.4%, precision of 91.2%, recall of90.8%, and F1-score of 91.0%. The system generates alerts within2–3 seconds upon detecting abnormal conditions, enabling timelyintervention.The proposed framework provides a scalable and efficientsolution for continuous healthcare monitoring and supportsproactive, data-driven decision-making.]]>
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