Abstract
We are living in the digital era, where most of the hospitals in the developed nations are keeping the medical records of the patients and as a result, most of the traits of the COVID-19 infected individuals are present in the digital form. Based upon the data thus generated, which is present on various platforms over the internet. In this chapter, an effort has been made to propose an artificial intelligence-based self-testing technique that can predict the patients who should go for COVID-19 testing. This chapter presents a belief rule-based expert system to predict the likelihood of the person to be tested for COVID-19. The system thus generated can easily pre-screen humans without the intervention of any second individual. Based upon the classification results the individual can be further tested to firm the presence of COVID-19 infection. This method will be cost-effective, plus it will also result in inefficient utilization of the scarce resource of medical testing kits.
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Arora, T., Soni, R. (2021). A Pre-screening Approach for COVID-19 Testing Based on Belief Rule-Based Expert System. In: Santosh, K., Joshi, A. (eds) COVID-19: Prediction, Decision-Making, and its Impacts. Lecture Notes on Data Engineering and Communications Technologies, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-15-9682-7_3
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DOI: https://doi.org/10.1007/978-981-15-9682-7_3
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