Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features

Seba Aziz Sahy, Sura Hammed Mahdi, Hassan Muwafaq Gheni, Israa Al-Barazanchi


COVID-19 is unquestionably one of the most hazardous health issues of our century, and it is a significant cause of mortality for both men and women throughout the globe. Even with the most advanced pharmacological and technical innovations, cancer oncologists, and biologists still have a substantial problem treating COVID-19. For patients with COVID-19, it is critical to offer initial, precise, and effective indicative procedures to increase their survival and minimize morbidity and mortality, which is currently lacking. A COVID-19 detection method has been presented in this paper for the initial identification of COVID-19 hazard factors. Features from accelerated segment test (FAST), a robust feature was used to extract features in this suggested method. The experiments show that it is possible to identify FAST traits efficiently. A consequence was a high success rate (98%) for accuracy performance.


COVID-19; Detection technique; FAST descriptor; Feature extraction

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