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Fig. 10 | BMC Neurology

Fig. 10

From: Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning

Fig. 10

Feature importance analysis using two machine learning methods: XGBoost and random forest model. A) corresponds to COVID-19, B) to AMI and C) to AIS. These plots show the rankings of feature importance derived from the XGBoost model (Plot A) and the Random Forest model (Plot B). In plot A, feature importance is assessed by gain (Gain), which indicates the contribution of each gene to the predictive power of the model. In plot B, importance is measured by mean accuracy decrease, which indicates the importance of each gene to the model's prediction accuracy

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BMC Neurology

ISSN: 1471-2377

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