A Multifaceted Approach to Understanding Mental Health Crises in the COVID-19 Era

Abstract

The COVID-19 pandemic, starting in Wuhan, China in December 2019, led to widespread health and economic challenges, causing millions of deaths globally. Beyond physical health, it triggered a mental health crisis, especially during lockdowns. To understand and address this, a study collected data using 90 features during the lockdown period. Machine learning (ML) was employed to detect key features impacting mental health crises. Three ML algorithms—random forest, random tree, and multilayer perceptron—were chosen. Random forest, known for robustness, achieved 97.58% accuracy. Random tree, a supervised algorithm with decision trees, yielded 93.24% accuracy. Multilayer perceptron (MLP), an artificial neural network, achieved 94.20% accuracy by learning nonlinear relationships. A 10-fold cross-validation method was used to evaluate these ML models, enhancing performance by reducing bias and overfitting. It involves dividing data into ten subsets, training on nine, and evaluating the remaining, repeating this ten times to estimate true performance on unseen data.