ABSTRACT
The pandemic of COVID-19 has had a significant impact on global health and is becoming a major international concern. Fortunately, early detection helped decrease its number of deaths. Artificial Intelligence (AI) and Machine Learning (ML) techniques are a new era, where the main objective is no longer to assist experts in decision-making but to improve and increase their capabilities and this is where interpretability comes in. This study aims to address one of the biggest hurdles that AI faces today which is public trust and acceptance due to its black-box strategy. In this paper, we use a deep Convolutional Neural Network (CNN) on chest computed tomography (CT) image data and Support Vector Machine (SVM) and Random Forest (RF) on clinical symptoms data (Bio-data) to diagnose patients positive for COVID-19. Our objective is to present an Explainable AI (XAI) models by using the Local Interpretable Model-agnostic Explanations (LIME) technique to identify positive patients to the virus in an interpreted way. The results are promising and outperformed the state of the art. The CNN model reached an Accuracy and F1-Score of 96% on CT-scan images, and SVM outperformed RF with Accuracy of 90% and Specificity of 91% on Bio-data. The interpretable results of XAI-Img-Model and XAI-Bio-Model, show that LIME explanations help to understand how SVM and CNN black box models behave in making their decision after being trained on different types of COVID-19 dataset. This can significantly increase trust and help experts understand and learn new patterns for the current pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.