SANN binary classification model to predict Post COVID-19 syndrome development

The source file of the ML-based binary classification model to predict Post COVID-19 syndrome development defined as self-reported persistence of new symptoms at 3 months after hospital discharge; the input dataset is required to include following variables:

-  Age = age (years);
-  Sex = sex  (2=Female, 1=Male); 
-  Tx-O2 = oxygen supplementation during treatment (2=Yes, 1=No);
-  CRP = peak C-reactive protein during hospitalization, mg/L;
-  eGFR = estimated glomerular filtration rate by CKD-EPI equation, mL/min/1,73m2;
-  Dyspnea after 6MWT, Visit 2 = Dyspnea at the end of the 6-minute walk test 1 month post-discharge as assessed using modified Borg scale (0-10 pts);
-  Fatigue after 6MWT, Visit 2 = Fatigue at the end of the 6-minute walk test 1 month post-discharge as assessed using modified Borg scale (0-10 pts);
-  MRC Dyspnea score, Visit 2 = MRC Dyspnea score at 1 month post-discharge.

The output codes for predicted status at 3 months post-discharge include “1” for absence and “2” for presence of the post COVID-19 syndrome.