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Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature

Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature

Yasmin Bouarara
Copyright: © 2023 |Pages: 11
ISBN13: 9781668456569|ISBN10: 1668456567|EISBN13: 9781668456576
DOI: 10.4018/978-1-6684-5656-9.ch007
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MLA

Bouarara, Yasmin. "Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature." Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform, edited by Hadj Ahmed Bouarara, IGI Global, 2023, pp. 143-153. https://doi.org/10.4018/978-1-6684-5656-9.ch007

APA

Bouarara, Y. (2023). Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature. In H. Bouarara (Ed.), Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform (pp. 143-153). IGI Global. https://doi.org/10.4018/978-1-6684-5656-9.ch007

Chicago

Bouarara, Yasmin. "Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature." In Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform, edited by Hadj Ahmed Bouarara, 143-153. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-5656-9.ch007

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Abstract

The detection of adverse events (spam) within a health establishment, associated or not with care, represents one of the axes of a risk management approach. It does not in any way constitute a means of denunciation, control, or sanction; the purpose being that the most important medical spam events do not recur. In this chapter the authors discuss the different algorithms of machine learning such as the KNN decision tree, naive bayes, etc., applicable to the filtering of medical spam event. The objective of these techniques is to control medical data in order to make decisions and achieve strategic objectives.

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