Abstract

Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

Highlights

  • The Internet of Things (IoT) and smart home technologies enable the monitoring of people in their homes without interfering with their daily routines [1]

  • This section describes in detail the progress and contributions, including the state-ofthe-art methods presented in the previous study on the detection of COVID-19

  • Based on some of the existing pitfalls of the previous study, this study presents a unique ensemble method using an automatic feature selection method based on Principal Component Analysis (PCA), improving the classification of models for efficient COVID-19 detection

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Summary

Introduction

The Internet of Things (IoT) and smart home technologies enable the monitoring of people in their homes without interfering with their daily routines [1]. Digital health signals recorded at home by sensors provide a wealth of clinical data Such data could be sent to cloud computing infrastructure and analyzed remotely, which is especially useful in the case of various contagious diseases, such as the coronavirus one [3]. The COVID-19 SARS-CoV-2 coronavirus pandemic has hit the world with more than 400 million confirmed cases and nearly 6 million deaths recorded It continues to have numerous negative consequences on health, society, and the environment [5]. The gold standard measure is the amplification of viral RNA by reverse transcription polymerase chain reaction (rRT-PCR) [6] It presents established weaknesses: lengthy processing times (3–4 h to deliver results), possible reagent shortages, insufficient RT-PCR test kits, high demand for experts [7], false negative rates of 15–20%, and the requirement for accredited laboratories, costly infrastructure, and qualified workers. Reliable new substitute tests, quicker, less costly, and more open tests, are required [8]

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