Viral Diseases mortality risk using machine learning
Abstract
Early prediction of patient mortality risks during a pandemic can minimise mortality by enabling appropriate resource allocation and treatment planning. The goal of this study was to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data acquired from patients on the day of admission. Three Support Vector Machine (SVM) models were developed and tested on invasive, non-invasive, and both patient groups. The findings indicated that non-invasive features could provide mortality estimates comparable to invasive features and on par with the joint model.
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