Application of Machine learning to investigate the mortality risk of viral diseases.

PAWAN WHIG

Abstract


Early identification of patient mortality concerns during a pandemic can lower mortality by facilitating efficient resource allocation and treatment planning. This study's objective was to develop and evaluate prognosis prediction machine learning models based on patient admission-day demographic, noninvasive clinical, and invasive laboratory data. Utilizing invasive, non-invasive, and both groups of patients, three Support Vector Machine (SVM) models were developed and contrasted. According to the results, non-invasive traits may provide mortality predictions that are comparable to invasive traits and about on par with the joint model.

References


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