Ensemble learning model for diagnosing COVID-19 from routine blood tests

The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6–100], AUC of 99.38% [95% CI: 97.5–100], a sensitivity of 98.72% [95% CI: 94.6–100] and a specificity of 99.99% [95% CI: 99.99–100]. The proposed model revealed better performance when compared against existing state-of-the-art studies (Banerjee et al., 2020; de Freitas Barbosa et al., 2020; de Moraes Batista et al., 2020; Soares et al., 2020) [3,22,56,71] for the same set of features employed by them. As compared to the best performing Bayes Net model (de Freitas Barbosa et al., 2020) [22] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model (de Moraes Batista et al., 2020) [56], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model (Soares et al., 2020) [71] a