Machine learning-based public health monitoring data anomaly detection and early warning system

Yuhan Jiang; Yuanlu Ding

doi:10.1117/12.3039591

4 September 2024 Machine learning-based public health monitoring data anomaly detection and early warning system

Yuhan Jiang, Yuanlu Ding

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592N (2024) https://doi.org/10.1117/12.3039591
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

Timely detection and effective control of potential public health hazards are crucial for public health monitoring. This paper proposes a machine learning-based monitoring data anomaly detection system, which can efficiently handle largescale monitoring data from multiple heterogeneous sources. The system architecture adopts a distributed cloud computing design, integrating advanced algorithms such as Isolation Forests and Long Short-Term Memory Neural Networks, to fully explore the spatiotemporal features of the data, achieving precise detection and prediction of abnormal events and development trends. Large-scale experiments have verified the outstanding performance of this system on real influenza, pneumonia, and other monitoring data, significantly outperforming traditional statistical methods, and providing strong support for public health decision-making.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuhan Jiang and Yuanlu Ding "Machine learning-based public health monitoring data anomaly detection and early warning system", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592N (4 September 2024); https://doi.org/10.1117/12.3039591

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