Authors: Andrei Nikolayevich Petrovski, Ekaterina Leonidovna Sokolova, Vladislav Dmitrievich Morozov, Irina Sergeyevna Volkova
Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating prompt and accurate risk detection for timely intervention. This research presents a scalable artificial intelligence (AI) infrastructure designed to support real-time cardiovascular risk detection using streaming medical data. The proposed architecture integrates distributed data ingestion, edge AI processing, and cloud-based model orchestration to ensure both low-latency diagnostics and high system reliability. Using a combination of convolutional neural networks (CNNs) for ECG signal analysis and gradient-boosted trees for patient history correlation, the system demonstrates improved predictive accuracy. Performance benchmarks show efficient scaling across multiple nodes, enabling high-throughput analysis essential for deployment in emergency and critical care settings. The paper evaluates model deployment on Kubernetes, real-time data flow with Apache Kafka, and compliance with healthcare data privacy regulations. The study concludes with recommendations for integrating this AI infrastructure into hospital networks and telemedicine platforms.
