TELKOMNIKA Telecommunication, Computing, Electronics and Control
IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models

TELKOMNIKA Telecommunication, Computing, Electronics and Control
IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models

AI
COVID-19 detection
False-negative RT-PCR
IgG
IgM
Machine learning
RT-PCR

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

Saja Dheyaa Khudhur, Dhuha Dheyaa Khudhur

Universitas Ahmad Dahlan

April 2022

Sri Wahyuni

ISSN: 1693-6930

PDF

English

Text