This paper considers the use of machine learning for diagnosis of diseases that is
based on the analysis of a complete gene expression profile. This distinguishes our study
from other approaches that require a preliminary step of finding a limited number of relevant
genes (tens or hundreds of genes). We conducted experiments with complete genetic expression
profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with
known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using
the indextron (instant learning index system) for a new purpose, i.e., for complete expression
profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of
histological verification.