Fixed point iterative algorithm with double inertial steps for solving data classification problems

The aim of this paper is to propose Krasnosel'skii-Mann type iteration with double inertial steps for approximating fixed points of nonexpansive mappings in real Hilbert spaces. The weak convergence is proved under some suitable conditions of the parameters. Some applications to the problems of finding a common fixed point of a family of mappings are also given. Finally, several numerical experiments to show the efficiency and accuracy of our method in breast and cervical cancer diseases predictions are presented.