  Quick and accurate medical diagnosis is crucial for the successful treatment
of a disease. Using machine learning algorithms, we have built two models to
predict a hematologic disease, based on laboratory blood test results. In one
predictive model, we used all available blood test parameters and in the other
a reduced set, which is usually measured upon patient admittance. Both models
produced good results, with a prediction accuracy of 0.88 and 0.86, when
considering the list of five most probable diseases, and 0.59 and 0.57, when
considering only the most probable disease. Models did not differ significantly
from each other, which indicates that a reduced set of parameters contains a
relevant fingerprint of a disease, expanding the utility of the model for
general practitioner's use and indicating that there is more information in the
blood test results than physicians recognize. In the clinical test we showed
that the accuracy of our predictive models was on a par with the ability of
hematology specialists. Our study is the first to show that a machine learning
predictive model based on blood tests alone, can be successfully applied to
predict hematologic diseases and could open up unprecedented possibilities in
medical diagnosis.
