CVD22: Explainable Artificial Intelligence Determination of the Relationship of Troponin to D-Dimer, Mortality, And CK-MB in COVID-19 Patients

COVID-19, which originated in Wuhan (China), is one of the deadliest and fastest-spreading pandemics as of the end of 2019. According to the World Health Organization, there are currently more than 100 million infectious cases worldwide with an average fatality rate of about 5%. The government must be aware of and foresee the pandemic's future trajectory in order to prevent catastrophic effects on people's lives and the economy. Because of this, research and forecasting models are crucial to managing the pandemic scenario. Nevertheless, because the behavior of this epidemic is so complicated and challenging to understand, an effective model must not only produce accurate predicting results but also have a clear explanation that enables human specialists to act proactively. With the recent development of Artificial Intelligence (AI) techniques and emerging Deep Learning (DL) models, this epidemic has proven quite effective in predicting the future from big historical data. With the pandemic data provided by Erzurum Training and Research Hospital, our xgboost model reached a 0.81 test score, 86% precision score, 83% f1-score, 80% recall score, and 0.91 AUC. A useful model was created to clear up the confusion about the low and high troponin levels and to make predictions in limited test times. Accordingly, throughout the continuing pandemic phase, this model may be utilized as a guide for authorities or medical experts to help them make the best judgments quickly. However, because the behavior of this pandemic is so complicated and challenging to understand, an effective model is taken into account not only for accurate predicting results but also for the explainable capacity for human specialists to take preventative action. The recent advancement of Artificial Intelligence (AI) methods and new Deep Learning (DL) models has made it possible to forecast the future pretty successfully using large historical data sets. Our model achieved a high R2 level and using the pandemic data supplied by Erzurum Training and Research Hospital. To eliminate the ambiguity around low and high troponin levels and to make predictions under constrained test timeframes, CVD22 was developed. Therefore, throughout the continuing pandemic phase, CVD22 may be utilized as a guide for authorities or medical experts to help them make the best judgments quickly.