After evaluating which combination of risk factors and algorithm provided the best performance, we concluded that the usage of thirteen risk factors (age, gender, serum iron concentration, serum ferritin concentration, transferrin saturation, uibc, HFE C282Y homozygosity, mean corpuscular volume, Asian ethnicity, number of relatives affected by haemochromatosis, red blood cell count, alanine aminotransferase serum activity, and aspartate aminotransferase serum activity) as an input dataset for an XGB classifier provided the most accurate results in the risk prediction of HH. This evidence concerns the gene TF and hereditary hemochromatosis.