The author of this document has limited its availability to on-campus or logged-in CSUSB users only.
Off-campus CSUSB users: To download restricted items, please log in to our proxy server with your MyCoyote username and password.
Date of Award
Restricted Project: Campus only access
Master of Science in Computer Science
School of Computer Science and Engineering
First Reader/Committee Chair
This Machine Learning algorithm offers a prediction methodology for potentially detecting severely affected COVID-19 patients based on blood sample parameters that are readily available. The development of effective and efficient treatment regimens for critical patients as well as routine patient monitoring for low-risk may be aided by these findings, which would also help to ease the patient flow in hospitals. They can be used to analyze how often hospital beds are used. Classifying COVID-19-affected patients as critical patients who need hospitalization or low-risk patients who might not need hospitalization is more accurate using the current machine learning-based methodologies.
Kajuluri, Raja, "HIGH-RISK PREDICTION FOR COVID-19 PATIENTS USING MACHINE LEARNING" (2022). Electronic Theses, Projects, and Dissertations. 1579.