An electronic device to which a
machine learning ensemble model is applicable, according to an exemplary embodiment of the present disclosure, may comprise: a first model neural-network-trained on the basis of training images; a second model trained on the basis of training
test data; a third model using voting based on a plurality of statistical methodologies so as to output the infection probability of
malaria infection; and a processor, which inputs, into the first model, a blood image to be tested, uses the first model to make a primary determination of
malaria infection about the blood image, inputs, into the second model, blood data to be tested, uses the second model to make a secondary determination of
malaria infection about the blood data, and uses the third model to output the malaria infection probability.