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This study aims to develop, validate, and evaluate machine learning algorithms for predicting the diagnosis of coronavirus disease (COVID-19), human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), pulmonary tuberculosis (TB), and HIV/TB co-infection. We also investigated potential biomarkers associated with the diagnosis. Data from biochemical and hematological tests of infected and controls were collected in a single general hospital, totalizing 6,418 patients. The discriminant analysis by partial least squares (PLS-DA) model had the highest performance in predicting the diagnosis of COVID-19, HIV/AIDS, TB, and HIV/TB co-infection with an accuracy of 94, 97, 95, and 96%, respectively. The biomarkers calcium, lactate dehydrogenase, red blood cells (RBC), white blood cells, neutrophils, basophils, eosinophils, hemoglobin, and hematocrit were associated with COVID-19. HIV infection was associated with mean corpuscular volume, platelets, neutrophils, and mean platelet volume. Red blood cell distribution width and urea were associated with infection by Mycobacterium tuberculosis. The following biomarkers were associated with HIV/TB co-infection: lymphocytes, RBC, hematocrit, hemoglobin, aspartate transaminase, alanine transaminase, and glycemia. The PLS-DA model can optimize COVID-19, HIV/AIDS, TB, and HIV/TB co-infection diagnostics. Some biomarkers were potential diagnostic indicators and could be evaluated during the screening of these diseases. develop validate COVID19, COVID19 COVID 19 , (COVID-19) virusacquired virus acquired HIVAIDS HIV/AIDS AIDS (HIV/AIDS) TB (TB) HIVTB coinfection. coinfection co infection. hospital 6418 6 418 6,41 patients PLSDA PLS DA (PLS-DA 19, COVID-19 94 97 95 96 96% respectively calcium dehydrogenase RBC (RBC) neutrophils basophils eosinophils hemoglobin COVID19. 19. volume platelets lymphocytes transaminase glycemia diagnostics diseases COVID1 1 (COVID-19 (HIV/AIDS (TB 641 41 6,4 COVID-1 9 (RBC (COVID-1 64 4 6, COVID- (COVID- (COVID