  Application of Machine Learning algorithms to the medical domain is an
emerging trend that helps to advance medical knowledge. At the same time, there
is a significant a lack of explainable studies that promote informed,
transparent, and interpretable use of Machine Learning algorithms. In this
paper, we present explainable multi-class classification of the Covid-19 mental
health data. In Machine Learning study, we aim to find the potential factors to
influence a personal mental health during the Covid-19 pandemic. We found that
Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy
of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF
and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable
Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance)
in their ability to explain the obtained Machine Learning results. To the best
of these authors knowledge, our study is the first explainable Machine Learning
study of the mental health data collected during Covid-19 pandemics.
