A Multi-Stage Approach Combining Feature Selection With Machine Learning

Abstract

The coronavirus pandemic is an unprecedented global crisis that poses not only a serious threat to physical health but also the challenge of mental illness. This case study highlights the multifaceted impacts of the mental illness epidemic by exploring the interplay of social, emotional, and economic factors. Through a combination of survey studies, qualitative interviews, and expert observations, this research highlights the range of mental health issues experienced by individuals from different groups of people. In this chapter, the authors presented a machine learning model that can investigate trust-related issues based on real-life data. The authors reduce computational complexity by trying to build models with fewer features. To develop the model, the authors followed four steps: collecting data through face-to-face interviews, asking for details, using different classification methods, and comparing and sharing the performance of different algorithms.