A method for in vitro diagnosing a complex disease namely acute myeloid leukemia, AML, in at least one biological sample of at least one tissue of a mammalian subject, wherein said mammalian subject is a human being, and said biological sample is/are blood and/or blood cells and/or bone marrow, comprising the steps of: a) measuring microRNA expression levels and CD34 presence in said sample and storing the obtained set of values as raw data in a database; b) mathematically preprocessing said raw data in order to reduce technical errors being inherent to the measuring procedures used in step a), wherein raw data of microRNA expression are preprocessed using a variance-stabilizing normalization and summarizing the normalized multiple probe signals to a single expression value, using the median and after preprocessing step b) a further step of feature selection is inserted, wherein a ranker, in particular a Mann-Whitney significance test combined with largest median pairwise differences, as filter for microRNA expression data is used, c) selecting a logistic regression classifier algorithm; d) said classifier algorithm of step c) being trained on at least one training data set containing preprocessed data from subjects being divided into classes according to their AML-types, in order to select a classifier function to map said preprocessed data to said AML-types, wherein the training data set of the classifier algorithm includes preprocessed and filtered microRNA expression data and CD34 presence, and the training is carried out with an n-fold cross-validation; e) applying said trained logistic regression classifier algorithm of step d) to predict the class label of said data set of step b) in order to diagnose the condition of the subject, namely a specific AML-type. a) measuring microRNA expression levels and CD34 presence in said sample and storing the obtained set of values as raw data in a database; b) mathematically preprocessing said raw data in order to reduce technical errors being inherent to the measuring procedures used in step a), wherein raw data of microRNA expression are preprocessed using a variance-stabilizing normalization and summarizing the normalized multiple probe signals to a single expression value, using the median and after preprocessing step b) a further step of feature selection is inserted, wherein a ranker, in particular a Mann-Whitney significance test combined with largest median pairwise differences, as filter for microRNA expression data is used, c) selecting a logistic regression classifier algorithm; d) said classifier algorithm of step c) being trained on at least one training data set containing preprocessed data from subjects being divided into classes according to their AML-types, in order to select a classifier function to map said preprocessed data to said AML-types, wherein the training data set of the classifier algorithm includes preprocessed and filtered microRNA expression data and CD34 presence, and the training is carried out with an n-fold cross-validation; e) applying said trained logistic regression classifier algorithm of step d) to predict the class label of said data set of step b) in order to diagnose the condition of the subject, namely a specific AML-type.