Smart Healthcare Monitoring System Using IoT Sensors and Deep Learning A Predictive Maintenance Approach
DOI:
https://doi.org/10.54228/mjaret0624033Keywords:
IoT sensors, healthcare monitoring, deep learning, predictive maintenance, edge computing, real-time monitoring, AI-driven analytics, blockchain security, patient care optimization, medical equipment maintenanceAbstract
An exponential rise in IoT healthcare devices combined with rising requirements for immediate patient monitoring needs advanced healthcare management solutions. An innovative patient monitoring platform links IoT sensors to deep learning algorithms in combination with edge computing technology for predictive healthcare diagnostics through predictive maintenance and patient monitoring operations. The system develops a networked security infrastructure that uses blockchain access security and AI anomaly identification to manage fast yet safe health data processing. The system underwent thorough experimental testing which produced patient anomaly detection accuracy at 92% while reaching 88% success in equipment maintenance predictions together with a 40% reduction of critical health event response times. Our system received a 35% increase in data processing speed because of edge computing integration and simultaneously experienced a 40% enhancement in security protocols. The resulting system produced substantial improvements to healthcare operations and presented better data protection methods and maintenance optimization of medical equipment. The healthcare system now achieves processing of real-time health data alongside high security standards because of its significant advancement in technology implementation.
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