A Multi-label Classification Study for the Prediction of Long-COVID Syndrome

Marco Dossena, Christopher Irwin, Luca Piovesan, Luigi Portinale

Abstract: We present a study about the prediction of long-COVID sequelae through multi-label classification (MLC). Data about more than 300 patients have been collected during a long-COVID study at Ospedale Maggiore of Novara (Italy), considering their baseline situation, as well as their condition on acute COVID-19 onset. The goal is to predict the presence of specific long-COVID sequelae after a one-year follow-up. To amplify the representativeness of the analysis, we carefully investigated the possibility of augmenting the dataset, by considering situations where different levels in the number of complications could arise. MLSmote under six different policies of data augmentation has been considered, and a representative set of MLC approaches have been tested on all the available datasets. Results have been evaluated in terms of Accuracy, Exact match, Hamming Score and macro-averaged AUC; they show that MLC methods can actually be useful for the prediction of specific long-COVID sequelae, under the different conditions represented by the different considered datasets.