Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests

Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from...

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Bibliographic Details
Main Authors: Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane, Farid Kadri, Ying Sun
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Diagnostics
Subjects:
COVID-19
routine blood tests
kernel PCA
semi-supervised anomaly detection
data-driven
Online Access:https://www.mdpi.com/2075-4418/13/8/1466

Internet

https://www.mdpi.com/2075-4418/13/8/1466

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