Machine Learning Role in Cognitive Mental Health Analysis amid Covid-19 Crisis: A Critical Study

Abstract

Coronavirus is a disease caused by SARS-CoV-2, which can cause severe respiratory problems in humans. World Health Organization declared it to be a pandemic, as per the rate of spread and scale of its transmission. The mental health of people is impacted rudely by Covid-19. The influence of the Covid19 virus on psychological health leads to depression, anxiety, posttraumatic stress, dementia, mental stress, helplessness, fear of losing, etc. Machine Learning is performing a critical role in the rapid advancement of the healthcare system in the past few years. Machine Learning techniques are employed to forecast, diagnose disease, evaluate data by studying the earlier data, and construct different patterns of it. Therefore, the purpose of this report is to address the issue of psychiatric illnesses by identifying those who are at an elevated risk of mental conditions, due to increased stress throughout the Covid19 crisis. Due to the current ongoing pandemic, the mental health crisis needs time, and proactive interference to confront and endure the anxiety. In this research, A comprehensive literature survey was undertaken to examine some machine learning predictive models for primitive prediction of a particular type of mental illness using machine learning algorithms. Several existing research papers were reviewed and after evaluation results show that among various algorithms like, Gradient Boosting Machine, Support Vector Machines, Naïve Bayes, K-Nearest Neighbors; Support Vector Machine (98.6%), Random Forest, and Random Forest (97.07%) are the most accurate algorithm for predicting mental illness amid the ongoing pandemic. © 2022 IEEE.