Accurate COVID-19 Detection Using Computing Models Using Artificial Intelligence and Big Data Analytics

Globally, the COVID-19 pandemic has devastated the health, social, economic, and societal systems and claimed many lives. Understanding the traits and behaviour of such an epidemic is necessary for control, and this can be done by gathering and examining relevant big data analytics (BDA) and artificial intelligence (AI). In order to ascertain the impact of coronavirus sickness across different age groups, we examine the most recent research on the illness. The effectiveness of Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) in identifying COVID-19 in patients based on their symptoms is also examined in the present research. The chosen machine learning (ML) techniques were applied to a pre-processed dataset that was gathered from a public repository. The findings show that every machine learning algorithm used effectively detects COVID-19 in prospective patients. With an accuracy of 96.12 and 95.53%, respectively, the RF and NB classifiers perform the best, while other algorithms, such SVM, show an accuracy of 94.75%. As a result, we conclude that ML approaches play a crucial part in identifying COVID-19 in patients by looking at their symptoms.

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