  The paper proposes and investigates an approach for surrogate-assisted
performance prediction of data-driven knowledge discovery algorithms. The
approach is based on the identification of surrogate models for prediction of
the target algorithm's quality and performance. The proposed approach was
implemented and investigated as applied to an evolutionary algorithm for
discovering clusters of interpretable clinical pathways in electronic health
records of patients with acute coronary syndrome. Several clustering metrics
and execution time were used as the target quality and performance metrics
respectively. An analytical software prototype based on the proposed approach
for the prediction of algorithm characteristics and feature analysis was
developed to provide a more interpretable prediction of the target algorithm's
performance and quality that can be further used for parameter tuning.
