IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models - Saja Dheyaa Khudhur; Dhuha Dheyaa Khudhur (Universitas Ahmad Dahlan, 2022)
Over the last two years, most scientists have been researching the solution to the pandemic coronavirus disease 2019 (COVID-19). So, the effective inspection and the rapid diagnosis of COVID-19 provide a mitigation ability to the burden on healthcare systems. These research works focus on detecting and knowing the history of infection in terms of time and developed symptoms. In infections detection, artificial intelligence (AI) technologies increase the accuracy and efficiency of the adopted detection methods. These methods will aid the medical staff in classifying patients, essentially when there is a healthcare resources shortage. This paper proposed machine learning-based models for detecting the time of COVID-19 infection in weeks using the laboratory factors of detected antibodies immunoglobulins G and immunoglobulins M (IgG-IgM). This test is common and helpful in diagnosing the suspected patients who held a negative result for the reverse transcription-polymerase chain reaction (RT-PCR) test. The proposed models consider two machine learning models adopting root mean square error (RMSE) and mean absolute error (MAE) factors. The results show acceptable efficiency of performance that ranges from 80% to 100% for pointing the patient in any week of infection, to reduce the likelihood of transmitting the infection from patients who have developed symptoms but with false-negative RT-PCR test.