Machine learning model distinguishes levels of psychological resilience in health care workers with 75% accuracy

A study published in Discover Artificial Intelligence used logistic regression, random forest and support vector machine (SVM) to classify health care workers according to their level of psychosocial resilience, using information collected during the COVID-19 pandemic. The logistic regression model produced the most accurate results, with an accuracy of 75.6% and an area under the ROC curve of 0.816, outperforming random forest (72.6%) and support vector machine (70.8%).

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