Open Access

ARTICLE

Anomaly Detection Using Data Rate of Change on Medical Data

Kwang-Cheol Rim¹, Young-Min Yoon², Sung-Uk Kim³, Jeong-In Kim^4,*

1 AI Convergence Research Institute, Chosun University, Gwangju, 61452, Republic of Korea
2 Interdisciplinary Program of Architectural Studies, Chonnam University, Gwangju, 61186, Republic of Korea
3 AINTCHAIN SOFT Co., Ltd., Mokpo-si, 58750, Republic of Korea
4 BK21 Human Resources Development Project Group, Chosun University, Gwangju, 61452, Republic of Korea

* Corresponding Author: Jeong-In Kim. Email:

Computers, Materials & Continua 2024, 80(3), 3903-3916. https://doi.org/10.32604/cmc.2024.054620

Received 03 June 2024; Accepted 02 August 2024; Issue published 12 September 2024

Abstract

The identification and mitigation of anomaly data, characterized by deviations from normal patterns or singularities, stand as critical endeavors in modern technological landscapes, spanning domains such as Non-Fungible Tokens (NFTs), cyber-security, and the burgeoning metaverse. This paper presents a novel proposal aimed at refining anomaly detection methodologies, with a particular focus on continuous data streams. The essence of the proposed approach lies in analyzing the rate of change within such data streams, leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy. Through empirical evaluation, our method demonstrates a marked improvement over existing techniques, showcasing more nuanced and sophisticated result values. Moreover, we envision a trajectory of continuous research and development, wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios, ensuring adaptability and robustness in real-world applications.

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Keywords

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