  Because of the rapid spread of COVID-19 to almost every part of the globe,
huge volumes of data and case studies have been made available, providing
researchers with a unique opportunity to find trends and make discoveries like
never before, by leveraging such big data. This data is of many different
varieties, and can be of different levels of veracity e.g., precise, imprecise,
uncertain, and missing, making it challenging to extract important information
from such data. Yet, efficient analyses of this continuously growing and
evolving COVID-19 data is crucial to inform -- often in real-time -- the
relevant measures needed for controlling, mitigating, and ultimately avoiding
viral spread. Applying machine learning based algorithms to this big data is a
natural approach to take to this aim, since they can quickly scale to such
data, and extract the relevant information in the presence of variety and
different levels of veracity. This is important for COVID-19, and for potential
future pandemics in general.
  In this paper, we design a straightforward encoding of clinical data (on
categorical attributes) into a fixed-length feature vector representation, and
then propose a model that first performs efficient feature selection from such
representation. We apply this approach on two clinical datasets of the COVID-19
patients and then apply different machine learning algorithms downstream for
classification purposes. We show that with the efficient feature selection
algorithm, we can achieve a prediction accuracy of more than 90\% in most
cases. We also computed the importance of different attributes in the dataset
using information gain. This can help the policy makers to focus on only
certain attributes for the purposes of studying this disease rather than
focusing on multiple random factors that may not be very informative to patient
outcomes.
