The study shows that machine-learning models can help predict COVID-19 infections.
to understand the correlation between the blood tests and molecular tests. The team used ML to discover which features are the most effective in distinguishing COVID-19 test results.The two types of tests have different processes to detect infection in an individual. The molecular test measures the prevalence of viral SARS-CoV-2 RNA, while the serology test detects the presence of antibodies triggered by SARS-CoV-2.
The research team noticed that the molecular test had the lowest positive rate because it measured the infection in its current state. The study also showed that the total number of days an individual has symptoms, such as fever or cough, played a large role in the test results.Another difference between the molecular test and serology test was the onset days of symptoms.
“One unique feature of our testbed is that some donors may have multiple test results, which allowed us to analyze the relationship between serology tests versus molecular tests, and also understand consistency within each type of test,” Zhu stated. The team created a set of features for predictive modeling using the five types of machine-learning models.The five models used were Random Forest, XGBoost, Logistic Regression, Support Vector Machine and Neural Network.
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