Machine Learning-based COVID-19 detection tool from complete blood count

This page is the online tool to diagnose COVID-19 from a complete blood count test, including other parameters such as the gender, age of the subject and the presence of suspect symptoms. The diagnosis is based on a predictive ML model.

Please fill in the all the fields of the following form:

(Fields marked with * are mandatory)

Attribute	Input value	Unit
Gender
Age *		Years
WBC (Leukocyte Count)		10^9/L
RBC (Red Blood Cells)		10^12/L
HGB (Hemoglobin)		g/dL
HCT (Hematocrit)		%
MCV (Average Global Volume)		fL
MCH (Mean Corpuscolar Hemoglobin)		pg/Cell
MCHC (Mean Corpuscolar Hemoglobin Concentration)		g Hb/dL
RDW (Red Cell Distribution Width)	(Not used in the predictive model)	CV%
PLT (Platelets)		10^9/L
MPV (Mean Platelet Volume)	(Not used in the predictive model)	fL
NET (Neutrophils Count)		10^9/L
LYT (Lymphocytes Count)		10^9/L
MOT (Monocytes Count)		10^9/L
EOT (Eosinophils Count)		10^9/L
BAT (Basophils Count)		10^9/L
NE (Neutrophils Count - Percent)		%
LY (Lymphocytes Count - Percent)		%
MO (Monocytes Count - Percent)		%
EO (Eosinophils Count - Percent)		%
BA (Basophils Count - Percent)		%
Suspect (put 'Yes' if the patient presents any of the main symptoms associated with COVID-19, like fever, dry cough, sore throat, loss of taste or smell, ...)
COVID-19 Prevalence (if known, otherwise leave blank)		%

DISCLAIMER

This calculator was created for research and testing purposes. Medical decisions must NOT be based on the results of this program, which cannot be considered a diagnostic tool. Although this program has been tested thoroughly, the accuracy of the information cannot be guaranteed and the authors shall not be liable for any claim, damages or other liability.

For technical details, please refer to: Cabitza F, Campagner A, Ferrari D, Di Resta C, Ceriotti D, Sabetta E, Colombini A, De Vecchi E, Banfi G, Locatelli M, Carobene A (2020). Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. CCLM, doi: 10.1515/cclm-2020-1294. Open access: https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1, Data available at: (https://zenodo.org/record/4081318#.X4V3btD7TIX)