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Ensemble learning model for diagnosing COVID-19 from routine blood tests
Maryam AlJame1, Imtiaz Ahmad1, Ayyub Imtiaz2
1Computer Engineering Department, Kuwait University, Kuwait.
Insights
A novel ensemble learning model, ERLX, accurately screens COVID-19 patients using routine blood tests. This rapid diagnostic tool demonstrates high accuracy, sensitivity, and specificity, aiding in early detection and disease control.
Area of Science:
- Medical Diagnostics
- Machine Learning in Healthcare
- Computational Biology
Background:
- The COVID-19 pandemic necessitates rapid and reliable diagnostic tools.
- Routine blood tests offer potential for early COVID-19 screening.
- Existing diagnostic methods require improvement for widespread application.
Purpose of the Study:
- To develop and validate an ensemble learning model for COVID-19 diagnosis using routine blood tests.
- To enhance the accuracy and efficiency of early COVID-19 screening.
- To provide a robust tool for timely patient care and disease containment.
Main Methods:
- An ensemble learning model (ERLX) was developed, combining Extra Trees, Random Forest, and Logistic Regression classifiers with an XGBoost meta-classifier.
- Data preprocessing involved KNNImputer for missing values, Isolation Forest for outlier detection, and SMOTE for class balancing.
- SHAP values were utilized for model interpretability and feature importance analysis.
Main Results:
- The ERLX model achieved exceptional performance on a public dataset, with 99.88% overall accuracy and 99.38% AUC.
- The model demonstrated high sensitivity (98.72%) and specificity (99.99%), crucial for reliable screening.
- ERLX significantly outperformed existing state-of-the-art models on the same features.
Conclusions:
- The ERLX model offers a robust and highly accurate solution for early COVID-19 screening using routine blood tests.
- Its superior performance metrics suggest potential for clinical deployment in rapid patient assessment.
- The model contributes to combating the COVID-19 pandemic through improved diagnostic capabilities.
Background And Objectives:
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.
Method:
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.
Results:
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].
Discussion:
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] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained a considerably higher score as compared with ANN model (Banerjee et al., 2020) [3] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.
