# Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data

arXiv: https://arxiv.org/abs/2105.13430 · HTML: https://mlsanity.com/paper/2105.13430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 7 of 7)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://mlsanity.com/paper/2005.01831/) (2020)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [Explainable Multi-class Classification of Medical Data](https://mlsanity.com/paper/2012.13796/) (2020)

## Built on by (most cited first, 2 of 2)

- [Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning](https://mlsanity.com/paper/2207.01012/) (2022)
- [Rumour Evaluation with Very Large Language Models](https://mlsanity.com/paper/2404.16859/) (2024)

## Abstract (arXiv)

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.