Smart Healthcare: Enhancement of Patient Outcomes By Iot-Enabled Wearable Devices and Machine Learning Algorithms

Abstract

By allowing constant, real-time monitoring and predictive analysis of patient health data, the integration of wearable devices enabled by Internet of Things (IoT) with Machine Learning (ML) algorithms is transforming healthcare. This work explores how early diagnosis, timely interventions, and tailored care plans combined with the synergy of wearable IoT devices and ML models might greatly improve patient outcomes. Using many ML approaches including Support Vector Machines (SVM), Random Forests, and Neural Networks, the architecture of smart healthcare systems is investigated with an eye toward sensor data acquisition, transmission, and intelligent processing. Open datasets and real-world case studies are examined to assess model performance in anomaly detection and disease progress forecast. The paper also covers important issues including data privacy, interoperability, energy economy, and HIPAA and HL7 compliance with regard. Results imply that the combination of wearable IoT devices with ML analytics has great possibilities to change healthcare delivery, lower hospitalization rates, and increase long-term patient monitoring in both clinical and remote environments.