Feature Selection for an Explainability Analysis in Detection of COVID-19 Active Cases from Facebook User-Based Online Surveys

Abstract

ABSTRACTIn this paper, we introduce a machine-learning approach to detecting COVID-19-positive cases from self-reported information. Specifically, the proposed method builds a tree-based binary classification model that includes a recursive feature elimination step. Based on Shapley values, the recursive feature elimination method preserves the most relevant features without compromising the detection performance. In contrast to previous approaches that use a limited set of selected features, the machine learning approach constructs a detection engine that considers the full set of features reported by respondents. Various versions of the proposed approach were implemented using three different binary classifiers: random forest (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). We consistently evaluate the performance of the implemented versions of the proposed detection approach on data extracted from the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS) for four different countries: Brazil, Canada, Japan, and South Africa, and two periods: 2020 and 2021. We also compare the performance of the proposed approach to those obtained by state-of-the-art methods under various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under ROC curve (AUC). It should be noted that the proposed machine learning approach outperformed state-of-the-art detection techniques in terms of the F1-score metric. In addition, this work shows the normalized daily case curves obtained by the proposed approach for the four countries. It should note that the estimated curves are compared to those reported in official reports. Finally, we perform an explainability analysis, using Shapley and relevance ranking of the classification models, to identify the most significant variables contributing to detecting COVID-19-positive cases. This analysis allowed us to determine the relevance of each feature and the corresponding contribution to the detection task.