An End-to-End Machine Learning Solution for Anxiety and Depressive Disorder Symptom Occurrence During COVID-19: A New York Case Study

Abstract

Anxiety and depression during the COVID-19 pandemic have heightened as evidenced by the rapidly growing corpus of research articles suggesting a link between the pandemic and mental health. This paper proposes a unique end-to-end user-centric machine learning (ML) architecture, capable of assessing the quality of ML predictions about the occurrence of anxiety and/or depression symptoms. A case study is presented using official New York State COVID-19 data, highlighting the plug-and-play capabilities of this architecture for both external features, and newer ML models. This is demonstrated through the formal design of a custom weighted clustering algorithm which outperforms conventional unsupervised techniques in grouping symptomatic cases. The ability to augment external sentiment data mined from social media platforms like Twitter, increases the predictive power of this architecture. This work serves as a blueprint to build a practical ML solution to better gauge the effect of future pandemic waves on mental health.