Depression Level Prediction in People with Parkinson's Disease during the COVID-19 Pandemic

Many recent studies show that the COVID-19 pandemic has been severely affecting the mental wellness of people with Parkinson's disease. In this study, we propose a machine learning-based approach to predict the level of anxiety and depression among participants with Parkinson's disease using surveys conducted before and during the pandemic in order to provide timely intervention. The proposed method successfully predicts one's depression level using automated machine learning with a root mean square error (RMSE) of 2.841. In addition, we performed model importance and feature importance analysis to reduce the number of features from 5,308 to 4 for maximizing the survey completion rate while minimizing the RMSE and computational complexity.