COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
Fig 12
Results for the training of models on various types of data.
Bar plot (A) and Radar plot (B) are sown. f1 scores obtained for four models designed to distinguish severe from non-severe COVID-19 cases. The first model was trained solely on biochemistry data, the second one on viral genomic data only, the third one was trained on a combined data sets made of biochemistry and viral data, while the last one is a model ensemble obtained by combining two models trained separately on the two types of datasets (biochemistry data and viral genome data). The data were merged and the model ensemble was designed using the soft voting function implemented in sklearn. Synthetic (oversampled) biochemistry data were used to increase the size of the biochemistry dataset used alongside the experimental viral genomic data.