A survey on detecting healthcare concept drift in AI/ML models from a finance perspective.
| Autor: | M S AR; Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India., C R N; Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India., B R S; Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India., Lahza H; Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia., Lahza HFM; Department of Information Systems, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Frontiers in artificial intelligence [Front Artif Intell] 2023 Apr 17; Vol. 5, pp. 955314. Date of Electronic Publication: 2023 Apr 17 (Print Publication: 2022). |
| DOI: | 10.3389/frai.2022.955314 |
| Abstrakt: | Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The healthcare industry is a major generator of data streams. Processing data streams is extremely complex due to factors such as volume, pace, and variety. Data stream classification is difficult owing to idea drift. Concept drift occurs in supervised learning when the statistical properties of the target variable that the model predicts change unexpectedly. We focused on solving various forms of concept drift problems in healthcare data streams in this research, and we outlined the existing statistical and machine learning methodologies for dealing with concept drift. It also emphasizes the use of deep learning algorithms for concept drift detection and describes the various healthcare datasets utilized for concept drift detection in data stream categorization. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 M. S., C. R., B. R., Lahza and Lahza.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|