engleski

Analysis of COVID-19 disease using machine learning - personalized model

Coronavirus disease (COVID-19), since its appearance, has put a large burden on the global health system which have strived to mitigate the pandemic, but mortality of COVID-19 continues to increase. Many authors have employed machine learning (ML) algorithms in the investigation of COVID-19 in order to identify infected individuals, predict their condition in time, predict the outbreaks and forecast certain numbers. Although there are many studies that examine the application of ML in the diagnosis of prognostic biomarkers and survival prediction several days in advance, there is a limited literature dealing with evidence to label patients in more categories (mild, moderate, severe, etc.) that would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not the case. In this paper we present a methodology for classification of patients into 3 distinct classes of clinical condition (mild, moderate and severe) of COVID-19 disease and prediction of the outcome (change of severity of clinical condition) in advance. The results show that XGBoost classifier achieved average accuracy of 88%. The main advantage of our system is that it is a rule-based algorithm which is easier to implement in a real clinical practice, instead of the use of black box models, which are not appealing for real clinical use.

COVID-19 ; personalized model ; clinical condition assessment ; ensemble model ; rule-based machine learning

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