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In Informatics in medicine unlocked

In this study, we utilized unsupervised machine learning techniques to examine the relationship between different symptoms in cases who died of COVID-19 and cases who recovered from it. In the preprocessing phase, our data was cleared of redundancies, and the ten most important variables were selected using a filter-based technique (extra-tree classifier). In the processing step, we calculated the Silhouette, Davis Boldin (DB), and the mean intra-cluster distance measures to select the optimal number of clusters, then clustered the data using both the K-means and hierarchical clustering based on Self Organizing Map (SOM) neural network. Our results revealed that patient who died of COVID-19 had high mean values in different symptoms, but not all patients with this characteristic necessarily died. Besides, our result indicated that the patient's age is directly related to the hospital duration, and elderly patients are more likely to be assigned to the intensive care unit (ICU), but the patient's sex has the same distribution in different groups and does not correlate with other symptoms. In conclusion, our results confirmed past research. Also, this study helps physicians improve medical services by considering other important factors for treating different groups of COVID-19 patients.

Ilbeigipour Sadegh, Albadvi Amir, Akhondzadeh Noughabi Elham


COVID-19, Clustering, Neural network, Self-organizing map, Unsupervised machine learning