Accurate COVID-19 detection using full blood count data and machine learning

Abstract

COVID-19 has spread rapidly worldwide in the past three years, triggering partial and full lockdowns globally. The successful control of the COVID-19 pandemic on a global scale depended heavily upon the accurate detection of COVID-19. However, the main diagnostic tests for COVID-19 have some significant limitations, e.g. the major nucleic acid (RT-PCR) tests while having a high sensitivity are time-consuming and require expensive equipment with the shortage of test kits in many countries. Antigen lateral flow tests have a lower sensitivity and they cannot be used during the early pandemic as well as usually more expensive than the full or complete blood count test used in this paper which can be potentially performed using a finger blood sample. The last decade has seen rapid growth of AI, particularly deep learning, which has found wide applications in medical image analysis, with results comparable to and even surpassing human expert performance. There have been several machine learning models reported for COVID-19 diagnostics or prognosis predictions, most of them based on CT and X-ray images. In this paper we have applied traditional machine learning and convolutional neural networks (CNNs) based deep learning to the blood test data obtained from hematology analyzers and demonstrated that the AI models can be used to detect COVID-19 with a high degree of accuracy (>97%). The performance of different classifiers will be compared and discussed. The work should have potential applications in current COVID-19 and future pandemics.

Keywords

• covid-19
• full blood count
• machine learning
• deep learning
• convolutional neural networks